Systems and methods for evaluating image quality

ABSTRACT

A method for evaluating image quality is provided. The method may include: obtaining an image, the image including a plurality of elements, each element of the plurality of elements being a pixel or voxel, each element having a gray level; determining, based on a maximum gray level of the plurality of elements, one or more thresholds for segmenting the image; determining one or more sub-images of a region of interest by segmenting, based on the one or more thresholds, the image; and determining, based on the one or more sub-images of the region of interest, a quality index for the image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a division of U.S. application Ser. No. 16/437,006,field on Jun. 11, 2019, which claims priority to Chinese PatentApplication No. 201811134373.2, filed on Sep. 27, 2018, Chinese PatentApplication No. 201811134375.1, filed on Sep. 27, 2018, Chinese PatentApplication No. 201811133622.6, filed on Sep. 27, 2018, Chinese PatentApplication No. 201811133609.0, filed on Sep. 27, 2018, and ChinesePatent Application No. 201810597965.1, filed on Jun. 11, 2018, thecontents of each of which are hereby incorporated by reference in itsentirety.

TECHNICAL FIELD

The present disclosure relates to image technology, and morespecifically relates to systems and methods for evaluating imagequality.

BACKGROUND

Angiography is an auxiliary examination technique that is widely used inthe diagnosis and treatment of various clinical diseases. Angiographycan help doctors to diagnose diseases in time, control the deteriorationof the diseases, and effectively improve the survival rate of patients.Therefore, the image quality of angiography images is crucial for thediagnosis of diseases.

In clinical application, a feasible way for the image quality evaluationof angiography images relies on a visual assessment of reconstructedimages by a user (e.g., a doctor, an imaging technician, a healthcareprovider) which may be subjective. In coronary angiography, the heartbeats can produce motion artifacts, and the doctors need to chooseappropriate reconstruction phase(s) to obtain qualified cardiac image(s)that can be used for diagnosis. Accordingly, a series of images may needto be generated by computer(s), and evaluated through a user interactioninterface. After the visual assessment by the user, a specificreconstruction phase may be selected for cardiac image reconstruction.However, the visual assessment of image quality in the image processingmay make the reconstruction process complicated, increase the burden onthe user for image quality assessment, cause inconsistent imageprocessing due to, e.g., variations between assessments by differentusers, slow down the image processing, make it difficult or impossibleto automate the image processing, and/or induce repetitious imagereconstruction and assessment.

Therefore, it is desirable to provide systems and methods for evaluatingimage quality automatically or semi-automatically, reconstructingcardiac images that have relatively high image qualities and areaffected by cardiac motion to a minimum extent, efficiently,cost-effectively, and without waste of time and/or resources.

SUMMARY

In one aspect of the present disclosure, a method for evaluating imagequality is provided. The method may include one or more of the followingoperations: obtaining an image, the image including a plurality ofelements, each element of the plurality of elements being a pixel orvoxel, each element having a gray level; determining, based on a maximumgray level of the plurality of elements, one or more thresholds forsegmenting the image; determining one or more sub-images of a region ofinterest by segmenting, based on the one or more thresholds, the image;and determining, based on the one or more sub-images of the region ofinterest, a quality index for the image.

In another aspect of the present disclosure, a method for reconstructinga target cardiac image is provided. The target cardiac image may includea plurality of elements, and each element of the plurality of elementsmay be a pixel or voxel. The method may include one or more of thefollowing operations: obtaining projection data generated by an imagingdevice, the projection data including a plurality of sub-sets ofprojection data, each sub-set of projection data corresponding to acardiac motion phase; obtaining a plurality of cardiac imagescorresponding to one or more cardiac motion phases based on theplurality of sub-sets of projection data corresponding to the one ormore cardiac motion phases; determining a quality index for each cardiacimage of the plurality of cardiac images; determining a phase ofinterest base on the plurality of quality indexes; and obtaining thetarget cardiac image of the phase of interest.

In another aspect of the present disclosure, a system for evaluatingimage quality is provided. The system may include at least one storagedevice storing a set of instructions; and at least one processor incommunication with the storage device, wherein when executing the set ofinstructions, the at least one processor may be configured to cause thesystem to perform one or more of the following operations: obtaining animage, the image including a plurality of elements, each element of theplurality of elements being a pixel or voxel, each element having a graylevel; determining, based on a maximum gray level of the plurality ofelements, one or more thresholds for segmenting the image; determiningone or more sub-images of a region of interest by segmenting, based onthe one or more thresholds, the image; and determining, based on the oneor more sub-images of the region of interest, a quality index for theimage.

In another aspect of the present disclosure, a system for reconstructinga target cardiac image is provided. The system may include at least onestorage device storing a set of instructions; and at least one processorin communication with the storage device, wherein when executing the setof instructions, the at least one processor may be configured to causethe system to perform one or more of the following operations: obtainingprojection data generated by an imaging device, the projection dataincluding a plurality of sub-sets of projection data, each sub-set ofprojection data corresponding to a cardiac motion phase; obtaining aplurality of cardiac images corresponding to one or more cardiac motionphases based on the plurality of sub-sets of projection datacorresponding to the one or more cardiac motion phases; determining aquality index for each cardiac image of the plurality of cardiac images;determining a phase of interest base on the plurality of qualityindexes; and obtaining the target cardiac image of the phase ofinterest.

In another aspect of the present disclosure, a system is provided. Thesystem may include an obtaining module configured to obtain an image,the image including a plurality of elements, each element of theplurality of elements being a pixel or voxel, each element having a graylevel; an ROI image extracting module configured to: determine, based ona maximum gray level of the plurality of elements, one or morethresholds for segmenting the image; and determine one or moresub-images of a region of interest by segmenting, based on the one ormore thresholds, the image; and an image quality evaluation moduleconfigured to determine, based on the one or more sub-images of theregion of interest, a quality index for the image.

In another aspect of the present disclosure, a system is provided. Thesystem may include an image selection module configured to obtainprojection data generated by an imaging device, the projection dataincluding a plurality of sub-sets of projection data, each sub-set ofprojection data corresponding to a cardiac motion phase; and obtain aplurality of cardiac images corresponding to one or more cardiac motionphases based on the plurality of sub-sets of projection datacorresponding to the one or more cardiac motion phases; a quality indexdetermination module configured to determine a quality index for eachcardiac image of the plurality of cardiac images; and an imagereconstruction module configured to determine a phase of interest baseon the plurality of quality indexes; and obtain the target cardiac imageof the phase of interest.

In another aspect of the present disclosure, a non-transitory computerreadable medium storing instructions is provided. The instructions, whenexecuted by at least one processor, may cause the at least one processorto implement a method including: obtaining an image, the image includinga plurality of elements, each element of the plurality of elements beinga pixel or voxel, each element having a gray level; determining, basedon a maximum gray level of the plurality of elements, one or morethresholds for segmenting the image; determining one or more sub-imagesof a region of interest by segmenting, based on the one or morethresholds, the image; and determining, based on the one or moresub-images of the region of interest, a quality index for the image.

In another aspect of the present disclosure, a non-transitory computerreadable medium storing instructions is provided. The instructions, whenexecuted by at least one processor, may cause the at least one processorto implement a method including: obtaining projection data generated byan imaging device, the projection data including a plurality of sub-setsof projection data, each sub-set of projection data corresponding to acardiac motion phase; obtaining a plurality of cardiac imagescorresponding to one or more cardiac motion phases based on theplurality of sub-sets of projection data corresponding to the one ormore cardiac motion phases; determining a quality index for each cardiacimage of the plurality of cardiac images; determining a phase ofinterest base on the plurality of quality indexes; and obtaining thetarget cardiac image of the phase of interest.

As illustrated above, the methods, systems, computing devices, andcomputer readable storage mediums for evaluating image qualities and/orreconstructing cardiac images, may determine a maximum gray level of animage (or each image), and/or designate the maximum gray levelmultiplied by one or more predetermined multiples as the threshold(s)for segmenting the image; and/or segment the image to be evaluated basedon the segmentation thresholds to obtain a vascular image of interest.According to the vascular images of interest, the quality indexes of thecorresponding images to be evaluated may be determined, and the imagequality of the images to be evaluated may be evaluated according to thequality indexes. The methods can automatically (or semi-automatically)evaluate image qualities of images of corresponding cardiac motionphases, simplify reconstruction processes, reduce the burden on thedoctor for image quality evaluation, avoid repeated image qualityevaluation, improve the accuracy of the mean phase or phase of interestdetermined for cardiac image reconstruction, and further improve thequality of the reconstructed cardiac images.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware andsoftware components of a computing device according to some embodimentsof the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device that is configured toimplement a specific system disclosed in the present disclosure;

FIG. 4 is a flowchart illustrating an exemplary process for evaluatingimage quality according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process forreconstructing an image according to some embodiments of the presentdisclosure;

FIG. 6 is another flowchart illustrating an exemplary process forreconstructing an image according to some embodiments of the presentdisclosure;

FIG. 7 is a flowchart illustrating an exemplary process for determininga mean phase according to some embodiments of the present disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for extractingan image of a region of interest according to some embodiments of thepresent disclosure;

FIG. 9 is a flowchart illustrating an exemplary process for extracting aventricular image according to some embodiments of the presentdisclosure;

FIG. 10 is a flowchart illustrating an exemplary process for extractinga blood vessel centerline associated with image(s) of a region ofinterest according to some embodiments of the present disclosure;

FIG. 11 is a flowchart illustrating an exemplary process for determiningimage(s) to be evaluated according to some embodiments of the presentdisclosure;

FIG. 12 is a flowchart illustrating an exemplary process for determininga quality index of an image to be evaluated according to someembodiments of the present disclosure;

FIG. 13A is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 13B is a block diagram illustrating another exemplary processingdevice according to some embodiments of the present disclosure; and

FIG. 14 is a block diagram illustrating an exemplary computing deviceaccording to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof. It will be understood that the term “object” and“subject” may be used interchangeably as a reference to a thing thatundergoes a treatment and/or an imaging procedure in a radiation systemof the present disclosure.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by anotherexpression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or themselves,and/or may be invoked in response to detected events or interrupts.Software modules/units/blocks configured for execution on computingdevices (e.g., processor 210 as illustrated in FIG. 2) may be providedon a computer-readable medium, such as a compact disc, a digital videodisc, a flash drive, a magnetic disc, or any other tangible medium, oras a digital download (and can be originally stored in a compressed orinstallable format that needs installation, decompression, or decryptionprior to execution). Such software code may be stored, partially orfully, on a storage device of the executing computing device, forexecution by the computing device. Software instructions may be embeddedin firmware, such as an EPROM. It will be further appreciated thathardware modules/units/blocks may be included in connected logiccomponents, such as gates and flip-flops, and/or can be included ofprogrammable units, such as programmable gate arrays or processors. Themodules/units/blocks or computing device functionality described hereinmay be implemented as software modules/units/blocks but may berepresented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description mayapply to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments of the presentdisclosure. It is to be expressly understood, the operations of theflowcharts may be implemented not in order. Conversely, the operationsmay be implemented in inverted order, or simultaneously. Moreover, oneor more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

For brevity, an image, or a portion thereof (e.g., a region of interest(ROI) in the image) corresponding to an object (e.g., a tissue, anorgan, a tumor, etc., of a subject (e.g., a patient, etc.)) may bereferred to as an image, or a portion thereof (e.g., an ROI) of orincluding the object, or the object itself. For instance, an ROIcorresponding to the image of a blood vessel may be described as thatthe ROI includes a blood vessel. As another example, an image of orincluding a blood vessel may be referred to a vascular image, or simplya blood vessel. For brevity, that a portion of an image corresponding toan object is processed (e.g., extracted, segmented, etc.) may bedescribed as the object is processed. For instance, that a portion of animage corresponding to a blood vessel is extracted from the rest of theimage may be described as that the blood vessel is extracted.

One aspect of the present disclosure relates to methods, systems,computing devices, and computer readable storage mediums for evaluatingimage qualities and/or reconstructing cardiac images, which may obtainan image; determine, based on a maximum gray level of the plurality ofelements of the image, one or more thresholds for segmenting the image;determine one or more sub-images of a region of interest by segmenting,based on the one or more thresholds, the image; and/or determine, basedon the one or more sub-images of the region of interest, a quality indexfor the image. The methods can include automatically orsemi-automatically evaluating image quality of a plurality of images,determining a mean phase or phase of interest based on the imagequalities, and/or reconstructing cardiac images based on the mean phaseor phase of interest, thereby simplifying reconstruction processes,reducing the burden on a user for image quality evaluation orassessment, avoiding repeated image quality evaluation or assessment,reducing inconsistency in image processing due to, e.g., variationsbetween assessments by different users, improving the efficiency of theimage processing, improving the accuracy of the mean phase or phase ofinterest for cardiac image reconstruction, and/or further improving thequality of the reconstructed cardiac images.

In order to make the objects, technical solutions and advantages of thepresent disclosure clearer, the present disclosure will be furtherdescribed in detail below with reference to the accompanying drawingsand embodiments. It should be understood that the specific embodimentsdescribed herein are merely illustrative of the present disclosure andare not intended to limit the present disclosure.

A computed tomography (CT) device may include a gantry, a scanning bed,and a console for the physician to operate. A tube may be disposed onone side of the gantry, and detectors may be disposed on a side oppositeto the tube. The console may include a computing device that controls CTscanning. The computing device may also be used to receive scan datacollected by the detectors, process the scan data and reconstruct CTimage(s). When scanning with CT, a patient may lie on the scanning bed,and the patient may be translated into the aperture of the gantry by thescanning bed. The tube disposed on the gantry may emit X-rays, and theX-rays may be received by the detectors to generate scan data. The scandata may be transmitted to the computing device, and the computingdevice may perform preliminary processing on the scan data and imagereconstruction to obtain CT image(s).

It should be noted that a relative position, e.g., left, right, upper,lower, above, under or underneath, or the like, in the presentdisclosure may refer to the relative positions in the image(s). Forexample, an upper position in an image may be closer to the upperboundary of the image than the lower position; a lower position in theimage may be closer to the lower boundary of the image than the upperposition. A left position in an image may be closer to the left boundaryof the image than the right position; a right position in an image maybe closer to the right boundary of the image than the left position.Furthermore, the sagittal axis (also referred to as the Y axis) mayrefer to the horizontal line in the anterior to posterior direction, thecoronal (frontal) axis (also referred to as the X axis) may refer to thehorizontal line in the left (of the object) to right (of the object)direction, and the vertical axis (also referred to as the Z axis) mayrefer to the perpendicular line in the superior to inferior direction,which is perpendicular to the horizontal line. And the sagittal planemay refer to the tangent plane along with the sagittal axis and verticalaxis, which may segment the object into left and right sections; thecoronal (frontal) plane may refer to the tangent plane along with thecoronal (frontal) axis and vertical axis, which may segment the objectinto anterior and posterior sections; and the transverse plane may referto the tangent plane along with the sagittal axis and coronal (frontal)axis, which may segment the object into superior and inferior sections.

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure. As shown inFIG. 1, the imaging system 100 may include a scanner 110, a network 120,one or more terminals 130, a processing device 140, and a storage device150. The components in the imaging system 100 may be connected in one ormore of various ways. Merely by way of example, the scanner 110 may beconnected to the processing device 140 through the network 120. Asanother example, the scanner 110 may be connected to the processingdevice 140 directly as indicated by the bi-directional arrow in dottedlines linking the scanner 110 and the processing device 140. As stillanother example, the storage device 150 may be connected to theprocessing device 140 directly or through the network 120. As stillanother example, the terminal 130 may be connected to the processingdevice 140 directly (as indicated by the bi-directional arrow in dottedlines linking the terminal 130 and the processing device 140) or throughthe network 120.

The scanner 110 may scan an object and/or generate scan data relating tothe object. In some embodiments, the scanner 110 may be asingle-modality medical imaging device (e.g., a magnetic resonanceimaging (MRI) device, a positron emission tomography (PET) device, asingle-photon emission computed tomography (SPECT) device, a computedtomography (CT) device, or the like) or a multi-modality medical imagingdevice (e.g., a PET-MRI device, a SPECT-MRI device, or a PET-CT device).In some embodiments, the scanner 110 may include a gantry configured toimaging the object, a detection region configured to accommodate theobject, and/or a scanning bed configured to support the object during animaging process. The scanning bed may support the object duringscanning. For example, the object may be supported and/or delivered tothe detection region of the gantry by the scanning bed. In someembodiments, the scanner 110 may transmit image(s) via the network 120to the processing device 140, the storage device 150, and/or theterminal(s) 130. For example, the image(s) may be sent to the processingdevice 140 for further processing or may be stored in the storage device150.

In some embodiments, the object may be biological or non-biological.Merely by way of example, the object may include a patient, an organ, atissue, a specimen, a man-made object, a phantom, etc. In someembodiments, the object to be scanned (also referred to as imaged) mayinclude a body, substance, or the like, or any combination thereof. Insome embodiments, the object may include a specific portion of a body,such as a head, a thorax, an abdomen, or the like, or any combinationthereof. In some embodiments, the object may include a specific organ,such as a breast, an esophagus, a trachea, a bronchus, a stomach, agallbladder, a small intestine, a colon, a bladder, a ureter, a uterus,a fallopian tube, etc. In the present disclosure, “object” and “subject”are used interchangeably.

The network 120 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the imaging system 100 (e.g., thescanner 110, the terminal 130, the processing device 140, the storagedevice 150, etc.) may communicate information and/or data with one ormore other components of the imaging system 100 via the network 120. Forexample, the processing device 140 may obtain image data from thescanner 110 via the network 120. As another example, the processingdevice 140 may obtain user instructions from the terminal 130 via thenetwork 120. The network 120 may be and/or include a public network(e.g., the Internet), a private network (e.g., a local area network(LAN), a wide area network (WAN)), etc.), a wired network (e.g., anEthernet network), a wireless network (e.g., an 802.11 network, a Wi-Finetwork, etc.), a cellular network (e.g., a Long Term Evolution (LTE)network), a frame relay network, a virtual private network (“VPN”), asatellite network, a telephone network, routers, hubs, switches, servercomputers, and/or any combination thereof. Merely by way of example, thenetwork 120 may include a cable network, a wireline network, afiber-optic network, a telecommunications network, an intranet, awireless local area network (WLAN), a metropolitan area network (MAN), apublic telephone switched network (PSTN), a Bluetooth™ network, aZigBee™ network, a near field communication (NFC) network, or the like,or any combination thereof. In some embodiments, the network 120 mayinclude one or more network access points. For example, the network 120may include wired and/or wireless network access points such as basestations and/or internet exchange points through which one or morecomponents of the imaging system 100 may be connected to the network 120to exchange data and/or information.

The terminal(s) 130 may include a mobile device 131, a tablet computer132, a laptop computer 133, or the like, or any combination thereof. Insome embodiments, the mobile device 131 may include a smart home device,a wearable device, a mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof. Insome embodiments, the smart home device may include a smart lightingdevice, a control device of an intelligent electrical apparatus, a smartmonitoring device, a smart television, a smart video camera, aninterphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a bracelet, a footgear,eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory,or the like, or any combination thereof. In some embodiments, the mobiledevice may include a mobile phone, a personal digital assistant (PDA), agaming device, a navigation device, a point of sale (POS) device, alaptop, a tablet computer, a desktop, or the like, or any combinationthereof. In some embodiments, the virtual reality device and/or theaugmented reality device may include a virtual reality helmet, virtualreality glasses, a virtual reality patch, an augmented reality helmet,augmented reality glasses, an augmented reality patch, or the like, orany combination thereof. For example, the virtual reality device and/orthe augmented reality device may include a Google Glass™, an OculusRift™, a Hololens™, a Gear VR™, etc. In some embodiments, theterminal(s) 130 may be part of the processing device 140.

The processing device 140 may process data and/or information obtainedfrom the scanner 110, the terminal 130, and/or the storage device 150.In some embodiments, the processing device 140 may be a single server ora server group. The server group may be centralized or distributed. Insome embodiments, the processing device 140 may be local or remote. Forexample, the processing device 140 may access information and/or datastored in the scanner 110, the terminal 130, and/or the storage device150 via the network 120. As another example, the processing device 140may be directly connected to the scanner 110, the terminal 130 and/orthe storage device 150 to access stored information and/or data. As afurther example, the processing device 140 may process the data obtainedfrom the scanner 110, evaluate image qualities, and/or reconstructcardiac images. In some embodiments, the processing device 140 may beimplemented on a cloud platform. Merely by way of example, the cloudplatform may include a private cloud, a public cloud, a hybrid cloud, acommunity cloud, a distributed cloud, an inter-cloud, a multi-cloud, orthe like, or any combination thereof. In some embodiments, theprocessing device 140 may be implemented by a computing device 200having one or more components as illustrated in FIG. 2. In someembodiments, the processing device 140, or a portion of the processingdevice 140 may be integrated into the scanner 110.

The storage device 150 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 150 may store dataobtained from the terminal 130 and/or the processing device 140. In someembodiments, the storage device 150 may store data and/or instructionsthat the processing device 140 may execute or use to perform exemplarymethods described in the present disclosure. In some embodiments, thestorage device 150 may include mass storage, removable storage, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. Exemplary mass storage devices may include amagnetic disk, an optical disk, a solid-state drive, etc. Exemplaryremovable storage devices may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memories may include a random access memory(RAM). Exemplary RAM may include a dynamic RAM (DRAM), a double daterate synchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), athyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc.

Exemplary ROM may include a mask ROM (MROM), a programmable ROM (PROM),an erasable programmable ROM (EPROM), an electrically erasableprogrammable ROM (EEPROM), a compact disk ROM (CD-ROM), and a digitalversatile disk ROM, etc. In some embodiments, the storage device 150 maybe implemented on a cloud platform. Merely by way of example, the cloudplatform may include a private cloud, a public cloud, a hybrid cloud, acommunity cloud, a distributed cloud, an inter-cloud, a multi-cloud, orthe like, or any combination thereof.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more other components in theimaging system 100 (e.g., the processing device 140, the terminal 130,etc.). One or more components of the imaging system 100 may access thedata or instructions stored in the storage device 150 via the network120. In some embodiments, the storage device 150 may be directlyconnected to or communicate with one or more other components of theimaging system 100 (e.g., the processing device 140, the terminal 130,etc.). In some embodiments, the storage device 150 may be part of theprocessing device 140.

FIG. 2 is a schematic diagram illustrating exemplary hardware andsoftware components of a computing device according to some embodimentsof the present disclosure. The computing device 200 may be a generalpurpose computer or a special purpose computer; both may be used toimplement an imaging system 100 of the present disclosure. In someembodiments, the processing device 140 may be implemented on thecomputing device 200, via its hardware, software program, firmware, or acombination thereof. Although only one such computer is shown, forconvenience, the computer functions as described herein may beimplemented in a distributed manner on a number of similar platforms, todistribute the processing load. As illustrated in FIG. 2, the computingdevice 200 may include a processor 210, a storage 220, an input/output(I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processor in accordance with techniquesdescribed herein. The computer instructions may include, for example,routines, programs, objects, components, data structures, procedures,modules, and functions, which perform particular functions describedherein. For example, the processor 210 may obtain an image; determine,based on a maximum gray level of the plurality of elements of the image,one or more thresholds for segmenting the image; determine one or moresub-images of a region of interest by segmenting, based on the one ormore thresholds, the image; and/or determine, based on the one or moresub-images of the region of interest, a quality index for the image.

In some embodiments, the processor 210 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, and thus operations and/or method steps that are performedby one processor as described in the present disclosure may also bejointly or separately performed by the multiple processors. For example,if in the present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 220 may store data/information obtained from the scanner110, the terminal 130, the storage device 150, and/or any othercomponent of the imaging system 100. In some embodiments, the storage220 may include a mass storage device, a removable storage device, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (EPROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 220 may store a program for scanning the heart ofthe object, a program for evaluating image qualities, and/or a programfor reconstructing cardiac images.

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing device 140. In some embodiments, the I/O 230 may include aninput device and an output device. Examples of the input device mayinclude a keyboard, a mouse, a touch screen, a microphone, or the like,or a combination thereof. Examples of the output device may include adisplay device, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Examples of the display device may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), a touch screen, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 120) to facilitate data communications. The communication port240 may establish connections between the processing device 140 and thescanner 110, the terminal 130, and/or the storage device 150. Theconnection may be a wired connection, a wireless connection, any othercommunication connection that can enable data transmission and/orreception, and/or any combination of these connections. The wiredconnection may include, for example, an electrical cable, an opticalcable, a telephone wire, or the like, or any combination thereof. Thewireless connection may include, for example, a Bluetooth™ link, aWi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee link, a mobile networklink (e.g., 3G, 4G, 5G, etc.), or the like, or a combination thereof. Insome embodiments, the communication port 240 may be and/or include astandardized communication port, such as RS232, RS485, etc. In someembodiments, the communication port 240 may be a specially designedcommunication port. For example, the communication port 240 may bedesigned in accordance with the digital imaging and communications inmedicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device that is configured toimplement a specific system disclosed in the present disclosure. Asillustrated in FIG. 3, the mobile device 300 may include a communicationunit 310, a display 320, a graphics processing unit (GPU) 330, a CPU340, an I/O 350, a storage 390, and a memory 360. In some embodiments,any other suitable component, including but not limited to a system busor a controller (not shown), may also be included in the mobile device300. In some embodiments, a mobile operating system 370 (e.g., IOS™,Android™, Windows Phone™, etc.) and one or more applications 380 may beloaded into the memory 360 from the storage 390 in order to be executedby the CPU 340. The applications 380 may include a browser or any othersuitable mobile apps for receiving and rendering information relating toimage processing or other information from the processing device 140.User interactions with the information stream may be achieved via theI/O 350 and provided to the processing device 140 and/or othercomponents of the imaging system 100 via the network 120. In someembodiments, a user may input parameters to the imaging system 100, viathe mobile device 300.

In order to implement various modules, units and their functionsdescribed above, a computer hardware platform may be used as hardwareplatforms of one or more elements (e.g., the processing device 140and/or other components of the imaging system 100 described in FIG. 1).Since these hardware elements, operating systems and program languagesare common; it may be assumed that persons skilled in the art may befamiliar with these techniques and they may be able to provideinformation needed in the imaging according to the techniques describedin the present disclosure. A computer with the user interface may beused as a personal computer (PC), or other types of workstations orterminal devices. After being properly programmed, a computer with theuser interface may be used as a server. It may be considered that thoseskilled in the art may also be familiar with such structures, programs,or general operations of this type of computing device.

FIG. 4 is a flowchart illustrating an exemplary process for evaluatingimage quality according to some embodiments of the present disclosure.In some embodiments, the process 400 may be executed by the imagingsystem 100. For example, the process 400 may be implemented as a set ofinstructions (e.g., an application) stored in one or more storagedevices (e.g., the storage device 150, the storage 220, and/or thestorage 390) and invoked and/or executed by the processing device 140(implemented on, for example, the processor 210 of the computing device200, and the CPU 340 of the mobile device 300). The operations of theprocess 400 presented below are intended to be illustrative. In someembodiments, the process 400 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 400 as illustrated in FIG. 4 and described below is notintended to be limiting.

In some embodiments, as shown in FIG. 4, an exemplary image qualityevaluation process is provided. The process 400 may include one or moreof the following operations:

In 4102, one or more images (e.g., images to be evaluated) may beobtained.

In some embodiments, the processing device 140 (e.g., the obtainingmodule 13100) may perform operation 4102. In some embodiments, eachimage of the one or more images may include a plurality of elements.Each element of the plurality of elements may be a pixel or voxel. Eachelement may have a gray level. In some embodiments, the image(s) may beobtained from the scanner 110, the storage device 150, an external datasource, etc. In some embodiments, the image(s) to be evaluated may beassociated with an object (e.g., a patient) or a portion thereof. Forexample, cardiac image(s) to be evaluated may include elements relatingto the thorax, the heart, one or more bones, one or more blood vessels,etc., of the object. In some embodiments, the image quality of an imagemay be evaluated based on one or more target portions of the image. Incardiac image reconstruction, the heart beats may produce motionartifacts into image(s), and the image quality may relate to one or moretarget portions of the object associated with heart beats (e.g., aventricle, a blood vessel (e.g., a coronary artery)). It should be notedthat in the following descriptions, image quality evaluation based onblood vessel(s) may be taken as an example for illustration purposes,any other target portion (e.g., a ventricle) may also be used as areference for image quality evaluation. Besides, the operationsillustrated below may also be used for the evaluation of other images(e.g., an abdomen image, a head image, a neck image, etc.). Forillustration purposes, a target portion of the object may be referred toas a target object, and a target portion of an image may be referred toas a target region.

Specifically, in some embodiments, a plurality of images to be evaluatedmay be obtained, a maximum gray level of the plurality of images (oreach of the plurality of images) may be determined, and/or the maximumgray level multiplied by one or more predetermined multiples may bedesignated as one or more thresholds for segmenting the images to beevaluated. In normal CT scanning, an object may be continuously scannedfor a period of time, and corresponding scan data may be obtained. Aplurality of images to be evaluated may be obtained based on the scandata. According to the obtained images to be evaluated, a maximum graylevel of an image (or each image) may be determined, and/or the maximumgray level multiplied by one or more predetermined multiples may bedesignated as the threshold(s) for segmenting the image. In someembodiments, the number (or count) of the predetermined multiples may beno less than 1. In some embodiments, the predetermined multiples may beno larger than 1 (e.g., 0.1, 0.2, 0.3, 0.4, 0.5, etc.). In someembodiments, before the threshold(s) are determined, one or moreoperations may be performed on the image(s) to improve the resolution(s)of the image(s). Improvement of the resolution(s) may cause theimprovement of the accuracy of determination of the blood vesselmorphologies and/or the blood vessel boundaries. In some embodiments,the resolution(s) of the image(s) may be improved using atwo-dimensional image interpolation algorithm.

In some embodiments, the threshold(s) for segmenting the images may alsobe referred to as segmentation threshold(s). In some embodiments, theresolution(s) of the image(s) may be improved based on one or morealgorithms. Exemplary algorithms may include a machine learningalgorithm, image super-resolution reconstruction algorithm, etc.

In 4104, one or more sub-images of a region of interest may bedetermined by segmenting, based on the one or more threshold(s), theimage(s).

In some embodiments, the processing device 140 (e.g., the image ofregion of interest extraction module (or ROI image extracting module)13200) may perform operation 4104. In some embodiments, a region ofinterest may refer to a target region in an image that has a relativelyhigh correlation with or impact on the image quality. In someembodiments, the region of interest may include blood vessel(s), andaccordingly, the sub-image(s) of the region of interest may include oneor more blood vessels. In some embodiments, a sub-image of the region ofinterest (e.g., the blood vessel(s)) may also be referred to as avascular image of interest.

In some embodiments, elements of an image (to be evaluated) with graylevel(s) larger than (and/or equal to) a segmentation threshold may beextracted as a vascular image of interest corresponding to thesegmentation threshold. In some embodiments, two or more vascular imagesof interest may be obtained by segmenting an image based on two or moresegmentation thresholds. For example, three segmentation thresholds maybe obtained according to the maximum gray level of an image multipliedby three predetermined multiples. In some embodiments, the image may besegmented based on a first segmentation threshold of the threesegmentation thresholds. For example, a region of the image havingelements with gray levels of greater than the first segmentationthreshold may be designated as a first vascular image of interest. Insome embodiments, the image may be segmented based on a secondsegmentation threshold of the three segmentation thresholds. Forexample, a region of the image having elements with gray levels greaterthan the second segmentation threshold may be designated as a secondvascular image of interest. In some embodiments, the image may besegmented based on a third segmentation threshold of the threesegmentation thresholds. For example, a region of the image havingelements with gray levels greater than the third segmentation thresholdmay be designated as a third vascular image of interest.

It should be noted that the number of the segmentation thresholds and/orthe vascular image of interest illustrated above is merely provided forthe purposes of illustration, and not intended to limit the scope of thepresent disclosure.

In 4106, image quality (or qualities) of the image(s) may be evaluatedbased on the one or more sub-images of the region of interest.

In some embodiments, the processing device 140 (e.g., the image qualityevaluation module 13300) may perform operation 4106. In someembodiments, the processing device 140 may determine a maximum qualityindex in the quality index(es) of the image(s); and/or designate animage that has the maximum quality index as a target image. The targetimage may have the optimal image quality among the image(s).

Specifically, in some embodiments, a quality index may be determined foran image (or each image) to be evaluated according to the vascularimage(s) of interest corresponding to the image, image qualityevaluation may be performed on the image according to the quality indexof the image.

More specifically, in some embodiments, to determine the quality indexof the image based on the vascular image(s) of interest corresponding tothe image, the processing device 140 may determine a regularity degreeof the image based on the vascular image(s) of interest. Morespecifically, in some embodiments, according to the vascular image(s) ofinterest, the perimeter and/or the area of a target region in the imageto be evaluated may be determined. For example, if the target regionincludes a blood vessel, the perimeter and/or the area of the bloodvessel in each vascular image of interest corresponding to the image tobe evaluated may be determined. The perimeters (and/or the areas) of theblood vessels in the vascular images of interest corresponding to theimage to be evaluated may be determined separately. In some embodiments,according to the perimeter(s) and/or the area(s) of the target region inthe image to be evaluated, the regularity degree of the image to beevaluated may be determined. In some embodiments, according to theedge(s) of the vascular image(s) of interest, and/or the gradient map(s)of the vascular image(s) of interest, a sharpness degree of the imagemay be determined. In some embodiments, the quality index of each imagemay be determined according to the regularity degree of the image and/orthe sharpness degree of the image. In some embodiments, the number oftarget regions (e.g., blood vessels) in different images may beinconsistent if the images are detected based on a same target object(e.g., at an identical physical position) but at different cardiacmotion phases. In some embodiments, the image quality evaluation mayneed to be performed on the images (e.g., the images obtained in 4102)based on a same reference (or criteria), that is, the number of targetregions (e.g., blood vessels) in the images that are detected based on asame target object (e.g., the blood vessels) at an identical physicalposition may need to be consistent. In some embodiments, a referenceparameter (e.g., the number of basic blood vessels, or the number ofblood vessels of a predetermined basic cardiac motion phase) may beintroduced. The reference parameter may refer to a parameter configuredto adjust the number (or count) of target regions to make the number (orcount) of target regions (e.g., blood vessels) in the images that aredetected based on a same target object (e.g., the blood vessels) at anidentical physical position to be consistent. In some embodiments,according to the number of basic blood vessels and/or the numbers ofblood vessels in the images to be evaluated, a regularity degree matrixof the images (to be evaluated) of each cardiac motion phase and asharpness degree matrix of the images (to be evaluated) of each cardiacmotion phase may be obtained. In some embodiments, the magnitudes of theregularity degree and the sharpness degree may be inconsistent.Therefore, it is desirable to adjust the regularity degree and thesharpness degree to a same baseline. In some embodiments, the regularitydegree and/or the sharpness degree may be adjusted based on a weightingprocess, a normalization process, or the like, or a combination thereof.

For example, the processing device 140 may designate a weighted sum ofthe regularity degree and the sharpness degree as the quality index forthe image. In some embodiments, each image of the image(s) obtained in4102 may be evaluated similarly as illustrated above.

In some embodiments, the regularity degree of an image may relate to themorphology of the image. In some embodiments, the regularity degree mayreflect an orderliness of the element(s) in an image to be evaluated.For example, the orderliness of a polygon may be lower than a circle,and accordingly, the regularity degree of the polygon may be lower thanthe circle. As another example, if an image has a relatively high levelof artifact(s), i.e., the clarity of the boundary (or boundaries) ofdifferent regions in the image is relatively low, then the regularitydegree of the image may be relatively low. In some embodiments, if animage has a relatively large regularity degree, the image may have arelatively high clarity (or quality) and/or a low level of artifacts. Insome embodiments, the sharpness degree of an image may relate to theclarity of the edge(s) of a target region of the image. If the edge(s)of the target region is relatively clear, the sharpness degree of theimage may be relatively large. If the edge(s) of the target region isrelatively blurry (e.g., the image has a relatively high level ofartifacts), the sharpness degree of the image may be relatively low. Animage having a relatively large quality index may have a relatively highregularity degree, and/or a relatively high sharpness degree, andaccordingly, the image may have a relatively fewer motion artifact,and/or a relatively high level of clarity. In some embodiments, if animage has a relatively large quality index, the image may have arelatively high image quality.

In some embodiments, image quality evaluation may be performed accordingto the quality indexes of the images to be evaluated, and the imagehaving the largest quality index may be selected as a target image thathas the optimum image quality.

In some embodiments, a plurality of images may be evaluated based on theevaluation process illustrated above. The quality indexes of theplurality of images may be determined. In some embodiments, theprocessing device 140 may determine one or more images having top N (inwhich N may be an integer larger than 0) quality indexes among theplurality of quality indexes as candidate target image(s). In someembodiments, the candidate target image(s) may be provided to a user(e.g., a doctor), and the user may select one or more target image(s)among the candidate target image(s). In some embodiments, the user mayset a quality index threshold, and the processing device 140 may providecandidate target image(s) having quality indexes larger than or equal tothe quality index threshold to the user for further determination of thetarget image(s). In some embodiments, the user may set the number N, andthe processing device 140 may provide the candidate target image(s)having the top N quality indexes to the user for further determinationof the target image(s).

According to the process for evaluating image quality described above, amaximum gray level (of an image) multiplied by one or more predeterminedmultiples may be designated as the thresholds for segmenting the imageto be evaluated, and the image to be evaluated may be segmented based onthe segmentation thresholds to obtain vascular image(s) of interest.According to the vascular image(s) of interest, the quality index of theimage to be evaluated may be determined, and the image qualities of theimages may be evaluated according to the quality indexes of the images.The automatic image quality evaluation of the image(s) based on thecorresponding quality indexes of the image(s) may reduce the burden onthe doctor for image quality evaluation, and further avoid repeatedimage reconstructions of a same image for image quality evaluation.

FIG. 5 is a flowchart illustrating an exemplary process forreconstructing an image according to some embodiments of the presentdisclosure. In some embodiments, the process 500 may be executed by theimaging system 100. For example, the process 500 may be implemented as aset of instructions (e.g., an application) stored in one or more storagedevices (e.g., the storage device 150, the storage 220, and/or thestorage 390) and invoked and/or executed by the processing device 140(implemented on, for example, the processor 210 of the computing device200, and the CPU 340 of the mobile device 300). The operations of theprocess 500 presented below are intended to be illustrative. In someembodiments, the process 500 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 500 as illustrated in FIG. 5 and described below is notintended to be limiting.

In some embodiments, as shown in FIG. 5, an exemplary process for imagereconstruction is provided. The process 500 may include one or more ofthe following operations:

In 5202, projection data of a plurality of cardiac motion phases may beobtained, and/or a plurality of images of the plurality of cardiacmotion phases may be reconstructed based on the projection data. In someembodiments, the plurality of images of the plurality of cardiac motionphases may be determined as the images to be evaluated.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 5202. The projection datamay include a plurality of sub-sets of projection data. In someembodiments, a sub-set of projection data may correspond to a cardiacmotion phase. In some embodiments, two or more sub-sets of projectiondata may correspond to a same cardiac motion phase. In some embodiments,the projection data may be generated by an imaging device (e.g., thescanner 110). In some embodiments, the imaging device may include a CTdevice. In some embodiments, the projection data may be obtained fromthe scanner 110, the storage device 150, an external data source, etc.In some embodiments, the processing device 140 may reconstruct aplurality of images corresponding to the plurality of cardiac motionphases based on the plurality of sub-sets of projection datacorresponding to the plurality of cardiac motion phases. In someembodiments, the plurality of cardiac motion phases may include alldiscrete cardiac motion phases. In some embodiments, the plurality ofcardiac motion phases may be sampled cardiac motion phases. In someembodiments, the images may be reconstructed using one or morereconstruction algorithms including, for example, FilteredBack-Projection (FBP), Algebraic Reconstruction Technique (ART), LocalReconstruction Algorithm (Local RA), and ordered-subset expectationmaximization (OSEM), etc. In some embodiments, a cardiac motion phasemay correspond to one or more images, while an image may correspond toone cardiac motion phase. In some embodiments, the images may be cardiacimages.

In some embodiments, cardiac motion phases may be denoted by percentagevalues (e.g., percentage values between 0%-100%). A phase x % maycorrespond to a phase angle x %*360°. In some embodiments, the pluralityof cardiac motion phases may be obtained at regular intervals (e.g., aninterval of 1%₇ 2%, 5%, 10%, 15%, 20%, 25%, etc.). For example, regularintervals may be intervals of 3%, 6%, 9%, 12%, 15%, 18%, 21%, etc. Insome embodiments, the plurality of sampled cardiac motion phases may beobtained at random intervals, irregular intervals, or by a certainpreset rule which may be determined by a user or the imaging system 100.For example, the plurality of cardiac motion phases may be obtained atthe random intervals from 1% to 100%. As another example, the pluralityof sampled cardiac motion phases may be obtained at different intervalsfor different cardiac cycles. In some embodiments, a cardiac cycle maybe divided into 100 phases from 1% to 100%. An exemplary regularinterval of the cardiac motion phases may be 12.5%, and accordingly, 8cardiac motion phases including 12.5%, 25%, 37.5%, 50%, 62.5%, 75%,87.5%, and 100% may be obtained.

Specifically, in some embodiments, in normal CT scanning, an object maybe continuously scanned for a period of time, and corresponding scandata (e.g., projection data) may be obtained. In a cardiac cycle, eachphase (e.g., each discrete cardiac motion phase) may have correspondingscan data (e.g., projection data) obtained by CT scanning. In someembodiments, each cardiac motion phase of 100 phases within 1%-100% ineach cardiac cycle may have corresponding scan data. The images of thecorresponding phases may be reconstructed based on the projection dataof the cardiac motion phases, respectively. In some embodiments, imagereconstruction may be performed based on a relatively smallreconstruction matrix and/or a relatively large layer thickness, but theimages (reconstructed based on the relatively small reconstructionmatrix and/or the relatively large layer thickness) may have arelatively low resolution, and may have a negative impact on subsequentsegmentation operation(s). In some embodiments, a relatively accuratereconstruction may be performed based on a reconstruction center (thatis automatically determined in the region of interest (e.g., bloodvessel(s))), a relatively small field of view (FOV), and/or a relativelylarge layer thickness. In some embodiments, a mean phase may bedetermined based on the images corresponding to the plurality of cardiacmotion phases. Images of cardiac motion phases in a preset rangeincluding the mean phase may be selected, and images of the region ofinterest may be extracted in the selected images. In some embodiments, ablood vessel centerline may be extracted in an image of the region ofinterest. In some embodiments, image segmentation may be performed onthe image(s) based on a preset region centered at the blood vesselcenterline, and a plurality of images to be evaluated may be obtained.

More descriptions of the reconstruction center and/or the reconstructionbased on a relatively small field of view (FOV) may be found in ChinesePatent Application No. 201810597965.1 entitled “METHODS, SYSTEMS, ANDCOMPUTING DEVICES FOR CARDIAC IMAGE RECONSTRUCTION,” filed on Jun. 11,2018, and U.S. application Ser. No. 16/437,003, entitled “SYSTEMS ANDMETHODS FOR RECONSTRUCTING CARDIAC IMAGES,” filed on Jun. 11, 2019, thecontents of which are hereby incorporated by reference.

In some embodiments, the mean phase may refer to a relatively optimalphase (in which the cardiac motion is relatively slight) for a pluralityof cardiac cycles (in which the projection data of the object aregenerated). In some embodiments, cardiac images of the mean phase mayhave a relatively low level of motion artifacts, a relatively highquality, and/or a relatively high clarity. More descriptions of thedetermination of the mean phase may be found elsewhere in the presentdisclosure (e.g., FIGS. 6-7 and descriptions thereof), or Chinese PatentApplication No. 201811133622.6 entitled “METHODS, SYSTEMS, COMPUTINGDEVICES, AND READABLE STORAGE MEDIA FOR CARDIAC IMAGE RECONSTRUCTION,”filed on Sep. 27, 2018, and Chinese Patent Application No.201811133609.0 entitled “METHODS, SYSTEMS, COMPUTING DEVICES, ANDREADABLE STORAGE MEDIA FOR CARDIAC IMAGE RECONSTRUCTION,” filed on Sep.27, 2018, and U.S. application Ser. No. 16/437,003, entitled “SYSTEMSAND METHODS FOR RECONSTRUCTING CARDIAC IMAGES,” filed on Jun. 11, 2019,the contents of which are hereby incorporated by reference. Moredescriptions of the determination of the images to be evaluated may befound elsewhere in the present disclosure (e.g., FIG. 6 and descriptionsthereof).

In 5204, a quality index of each image of the plurality of images may bedetermined.

In some embodiments, the processing device 140 (e.g., the quality indexdetermination module 13500) may perform operation 5204. In someembodiments, operation 5204 may be performed according to one or moreoperations (e.g., operations 4104 and 4106) described in FIG. 4. Moredescriptions of the determination of the quality index may be foundelsewhere in the present disclosure (e.g., FIG. 12 and descriptionsthereof).

Specifically, in some embodiments, a maximum gray level of the images(or each image) to be evaluated may be determined, and/or the maximumgray level multiplied by one or more predetermined multiples may bedesignated as segmentation thresholds. According to the segmentationthresholds, each image may be segmented to obtain a plurality ofvascular images of interest. A quality index of each image to beevaluated may be determined according to the vascular images of interestobtained from the image. Therefore, in each cardiac cycle, a pluralityof quality indexes of the images (to be evaluated) of the plurality ofcardiac motion phases may be obtained.

In 5206, a phase of interest may be determined based on the plurality ofquality indexes, and/or one or more target images of the phase ofinterest may be obtained.

In some embodiments, the processing device 140 (e.g., the imagereconstruction module 13600) may perform operation 5206. In someembodiments, the processing device 140 may determine a maximum qualityindex in the plurality of quality indexes (in each cardiac cycle), anddesignate the phase of an image that has the maximum quality index asthe phase of interest.

The phase of interest may refer to a relatively optimal phase (in whichthe cardiac motion is relatively slight) for each cardiac cycle. Thephases of interest for different cardiac cycles may be the same ordifferent. The phase of interest may be the same as or different fromthe mean phase. For example, a phase of interest in a first cardiaccycle may be the same as the mean phase. As another example, a phase ofinterest in a second cardiac cycle may be less than the mean phase. As afurther example, a phase of interest in a third cardiac cycle may belarger than the mean phase. More descriptions of the determination ofthe phase of interest may be found elsewhere in the present disclosure(e.g., FIG. 6 and descriptions thereof), or Chinese Patent ApplicationNo. 201811133622.6 entitled “METHODS, SYSTEMS, COMPUTING DEVICES, ANDREADABLE STORAGE MEDIA FOR CARDIAC IMAGE RECONSTRUCTION,” filed on Sep.27, 2018, and Chinese Patent Application No. 201811133609.0 entitled“METHODS, SYSTEMS, COMPUTING DEVICES, AND READABLE STORAGE MEDIA FORCARDIAC IMAGE RECONSTRUCTION,” filed on Sep. 27, 2018, and U.S.application Ser. No. 16/437,003, entitled “SYSTEMS AND METHODS FORRECONSTRUCTING CARDIAC IMAGES,” filed on Jun. 11, 2019, the contents ofwhich are hereby incorporated by reference.

Specifically, in some embodiments, in each cardiac cycle, the image thathas the maximum quality index may be selected, and accordingly, thephase of the image that has the maximum quality index in the cardiaccycle may be determined as the phase of interest in the cardiac cycle.In some embodiments, an image reconstructed at the phase of interest maybe determined as a target cardiac image of the phase of interest (in thecardiac cycle). In some embodiments, images of the plurality of cardiacmotion phases may be reconstructed based on the projection data of theplurality of cardiac motion phases, the image with the maximum qualityindex may be selected, and the phase of the image with the maximumquality index may be determined as the phase of interest. In someembodiments, the reconstructed image of the phase of interest may beselected as the target image of the phase of interest. In someembodiments, the image with the maximum quality index may be directlyselected as the target image of the phase of interest.

In some embodiments, in order to determine the mean phase or phase ofinterest, images to be evaluated may be reconstructed based onprojection data and a reconstruction center (that is automaticallydetermined in the region of interest (e.g., blood vessel(s))), arelatively small field of view (FOV), and/or a relatively large layerthickness, thereby reducing the amount of data involved in computation,saving computing resources, and/or improving processing efficiency. Insome embodiments, after the mean phase or phase of interest isdetermined, image(s) may be reconstructed based on a regularreconstruction center (e.g., the rotation center of the scanner 110), arelatively large FOV, and/or a relatively small layer thickness, andaccordingly, the reconstructed images may have more detail information,thereby facilitating the diagnosis of diseases.

According to the process described above, image reconstruction may beperformed based on projection data of the plurality of cardiac motionphases to obtain images of the plurality of cardiac motion phases. Theimage(s) to be evaluated of a specific phase may be obtained from allthe images corresponding to the plurality of cardiac motion phases.According to one or more image quality evaluation rules, the qualityindex of each image to be evaluated may be determined, the phase ofinterest may be determined according to the quality indexes of images tobe evaluated of the plurality of cardiac motion phases, and the targetimage(s) of the phase of interest may be reconstructed. The operationsillustrated above does not rely on user interface interaction, and canautomatically detect and extract the images to be evaluated, andautomatically analyze the image quality of the images (e.g., vascularimages). In coronary angiography, the process can be performed toautomatically select the phase of interest. Users (e.g., doctors) arenot required to evaluate image(s) and choose one or more phases ofinterest for image reconstruction. Therefore, the coronaryreconstruction process may be simplified, and the users' time toevaluate image(s) and/or select parameters (e.g., the phase of interest)may be saved.

It should be noted that the above description of process 500 is merelyprovided for the purposes of illustration, and not intended to limit thescope of the present disclosure. For persons having ordinary skills inthe art, multiple variations and modifications may be made under theteachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, operation 5202 may be decomposed into two operations, inwhich a first operation may obtain projection data, and a secondoperation may reconstruct the plurality of images of the plurality ofcardiac motion phases. As another example, operation 5206 may bedecomposed into two operations, in which a first operation may determinethe phase of interest, and a second operation may obtain target image(s)of the phase of interest.

FIG. 6 is another flowchart illustrating an exemplary process forreconstructing an image according to some embodiments of the presentdisclosure. In some embodiments, the process 600 may be executed by theimaging system 100. For example, the process 600 may be implemented as aset of instructions (e.g., an application) stored in one or more storagedevices (e.g., the storage device 150, the storage 220, and/or thestorage 390) and invoked and/or executed by the processing device 140(implemented on, for example, the processor 210 of the computing device200, and the CPU 340 of the mobile device 300). The operations of theprocess 600 presented below are intended to be illustrative. In someembodiments, the process 600 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 600 as illustrated in FIG. 6 and described below is notintended to be limiting. In some embodiments, operation 5202 of FIG. 5may be performed according to one or more operations of the process 600in FIG. 6.

In some embodiments, as shown in FIG. 6, an exemplary process for imagereconstruction is provided. The process 600 may include one or more ofthe following operations:

In 6302, projection data of a plurality of cardiac motion phases may beobtained, and/or a plurality of initial images may be reconstructedbased on the projection data.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 6302. The projection datamay include a plurality of sub-sets of projection data. In someembodiments, a sub-set of projection data may correspond to a cardiacmotion phase. In some embodiments, two or more sub-sets of projectiondata may correspond to a same cardiac motion phase. In some embodiments,the projection data may be generated by an imaging device (e.g., thescanner 110). In some embodiments, the imaging device may include a CTdevice. In some embodiments, the projection data may be obtained fromthe scanner 110, the storage device 150, an external data source, etc.In some embodiments, the plurality of cardiac motion phases may includeall the cardiac motion phases. In some embodiments, the plurality ofcardiac motion phases may be sampled cardiac motion phases. In someembodiments, the plurality of initial images may include cardiac images.In some embodiments, one or more initial images may correspond to a samecardiac motion phase. In some embodiments, one or more cardiac images tobe evaluated may be selected from the plurality of initial images.

Specifically, in some embodiments, in normal CT scanning, an object maybe continuously scanned for a period of time, and corresponding scandata (e.g., projection data) may be obtained. In a cardiac cycle, eachphase (e.g., each discrete cardiac motion phase) may have correspondingscan data (e.g., projection data) obtained by CT scanning. In someembodiments, each cardiac motion phase of 100 phases within 1%-100% ineach cardiac cycle may have corresponding scan data. The initial imagesof the corresponding phases may be reconstructed based on the projectiondata of the plurality of cardiac motion phases, respectively.

In some embodiments, as illustrated above, image reconstruction may beperformed based on a relatively small reconstruction matrix and/or arelatively large layer thickness.

In 6304, a mean phase may be determined based on the plurality ofinitial images.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 6304.

Specifically, in some embodiments, cardiac motion parameters of theplurality of cardiac motion phases may be determined according to theinitial images of the plurality of cardiac motion phases. The mean phasemay be determined according to the cardiac motion parameters of theplurality of cardiac motion phases. The mean phase may include a meanphase of a systolic period, and/or a mean phase of a diastolic period.

In some embodiments, the processing device 140 may determine a cardiacmotion parameter corresponding to each cardiac motion phase of theplurality of cardiac motion phases based on an initial cardiac image ofthe plurality of initial cardiac images. The processing device 140 maydetermine the mean phase based on the plurality of cardiac motionparameters corresponding to the plurality of cardiac motion phases. Insome embodiments, a cardiac motion parameter may refer to a parameterdescribing the cardiac motion. In some embodiments, the cardiac motionparameter may be associated with a cardiac motion rate or intensity.Exemplary cardiac motion parameters may include a cardiac motion rate, acardiac motion intensity, etc. The cardiac motion rate may include ablood flow rate in a blood vessel of the heart, a muscle contractionrate of a cardiac muscle, etc. The cardiac motion intensity may includea magnitude of vasoconstriction, a magnitude of vasodilation, aheartbeat amplitude, etc. In some embodiments, the cardiac motionparameter may refer to a parameter associated with the cardiac motionrate or the cardiac motion intensity. For example, the parameter may bethe cardiac motion rate (or the cardiac motion intensity) multiplied bya coefficient. As another example, the parameter may relate to areciprocal of the cardiac motion rate (or the cardiac motion intensity).In some embodiments, if the cardiac motion parameter is relativelylarge, the cardiac motion may be relatively pronounced. In someembodiments, if the cardiac motion parameter is relatively small, thecardiac motion may be relatively pronounced. More descriptions of thedetermination of the cardiac motion parameters may be found elsewhere inthe present disclosure (e.g., FIG. 7 and descriptions thereof), orChinese Patent Application No. 201811133622.6 entitled “METHODS,SYSTEMS, COMPUTING DEVICES, AND READABLE STORAGE MEDIA FOR CARDIAC IMAGERECONSTRUCTION,” filed on Sep. 27, 2018, and Chinese Patent ApplicationNo. 201811133609.0 entitled “METHODS, SYSTEMS, COMPUTING DEVICES, ANDREADABLE STORAGE MEDIA FOR CARDIAC IMAGE RECONSTRUCTION,” filed on Sep.27, 2018, and U.S. application Ser. No. 16/437,003, entitled “SYSTEMSAND METHODS FOR RECONSTRUCTING CARDIAC IMAGES,” filed on Jun. 11, 2019,the contents of which are hereby incorporated by reference.

In 6306, one or more candidate cardiac motion phases in a preset phaserange may be selected, and/or an image of a region of interest may beobtained by extracting the region of interest in each initial image ofthe preset phase range. In some embodiments, the preset range mayinclude the mean phase.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 6306. In some embodiments,the region of interest may include blood vessel(s), and accordingly, animage of the region of interest may refer to a vascular image ofinterest. In some embodiments, before extracting the region of interestin the initial image(s) of the preset range, the initial image(s) may besmoothed (e.g., using a low-pass filter) to facilitate furtherprocessing. Exemplary low-pass filters may include a Butterworth filter,a Chebyshev filter, a Gaussian filter, etc.

In some embodiments, the cardiac motion phases selected in the presetrange may include part or all of the phases in the preset range. Forexample, if the mean phase is 45%, and the preset range is 40%-50%, thenthe cardiac motion phase(s) in the preset range 40%-50% may be selected(e.g., 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%). In someembodiments, the preset range may be 5%, 10%, 20% (or the like) aroundthe mean phase. For example, in each cardiac cycle, phases within 10%around the mean phase may be selected, and images corresponding to thephases within 10% around the mean phase in each cardiac cycle may bereconstructed. Merely by way of example, if the mean phase is M %, andthe preset range is 2N %, then the cardiac motion phases from (M−N) % to(M+N) % (i.e., phases within 2N % around the mean phase) may beselected.

Specifically, in some embodiments, the initial image(s) in the presetphase range including the mean phase may be selected, and/or theselected initial image(s) in the preset range may be smoothed by theGaussian low-pass filter. In some embodiments, a ventricular image maybe extracted from a smoothed image. A threshold associated with graylevel(s) of a contrast agent (in the plurality of initial images) may bedetermined based on one or more ventricular images. Image segmentationmay be performed on the ventricular image(s) according to the thresholdassociated with the gray level of the contrast agent to obtain one ormore contrast agent images. In some embodiments, and an image of theregion of interest may be determined based on a contrast agent image.

More descriptions of the determination of the image(s) of the region ofinterest may be found elsewhere in the present disclosure (e.g., FIG. 8and descriptions thereof), or Chinese Patent Application No.201811133622.6 entitled “METHODS, SYSTEMS, COMPUTING DEVICES, ANDREADABLE STORAGE MEDIA FOR CARDIAC IMAGE RECONSTRUCTION,” filed on Sep.27, 2018, and Chinese Patent Application No. 201811133609.0 entitled“METHODS, SYSTEMS, COMPUTING DEVICES, AND READABLE STORAGE MEDIA FORCARDIAC IMAGE RECONSTRUCTION,” filed on Sep. 27, 2018, and U.S.application Ser. No. 16/437,003, entitled “SYSTEMS AND METHODS FORRECONSTRUCTING CARDIAC IMAGES,” filed on Jun. 11, 2019, the contents ofwhich are hereby incorporated by reference.

In 6308, a blood vessel centerline associated with the image(s) of theregion of interest may be identified according to the image(s) of theregion of interest obtained in 6306.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 6308. More descriptions ofthe blood vessel centerline and the identification of the blood vesselcenterline may be found elsewhere in the present disclosure (e.g., FIG.10 and descriptions thereof).

Specifically, in some embodiments, one or more images in a coronal planeand/or one or more images in a sagittal plane may be generated based onthe image(s) of the region of interest. In some embodiments, a bloodvessel main body may be determined according to the image(s) in thecoronal plane and/or the image(s) in the sagittal plane. In someembodiments, one or more false positive vessels may be filtered out fromthe blood vessel main body. In some embodiments, the position of a bloodvessel center in each transverse layer associated with the image(s) ofthe region of interest may be determined, and accordingly, the bloodvessel centerline associated with the image(s) of the region of interestmay be obtained based on the position of the blood vessel center in theeach transverse layer.

In 6310, the images to be evaluated may be obtained by segmenting, basedon a preset region including the blood vessel centerline, the image(s)of the region of interest.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 6310. In some embodiments,the preset region may be centered at the blood vessel centerline.

Specifically, in some embodiments, a top-hat transformation may beperformed on each image of the region of interest to obtain atransformed image of the region of interest. The transformed image ofthe region of interest may mainly include information of a target object(e.g., a blood vessel). According to a threshold associated with graylevel(s) of soft tissue(s), the (transformed) image(s) of the region ofinterest may be segmented to obtain image(s) that reserve theintraventricular region. In some embodiments, elements of the image(s)that reserve the intraventricular region and within a preset regionincluding the blood vessel centerline may be extracted as the image(s)to be evaluated.

More descriptions of the determination of the images to be evaluated maybe found elsewhere in the present disclosure (e.g., FIG. 11 anddescriptions thereof).

In 6312, the quality index for each image to be evaluated may bedetermined. In some embodiments, the quality index(es) may be determinedbased on one or more image quality evaluation rules described in thepresent disclosure.

In some embodiments, the processing device 140 (e.g., the quality indexdetermination module 13500) may perform operation 6312. In someembodiments, operation 6312 may be performed similarly to the operations4104 and/or 4106.

Specifically, in some embodiments, a maximum gray level of the images(or each image) to be evaluated may be determined, and/or the maximumgray level multiplied by one or more predetermined multiples may bedesignated as segmentation thresholds. According to the segmentationthresholds, each image may be segmented to obtain a plurality ofvascular images of interest. A quality index of each image to beevaluated may be determined according to the vascular images of interestobtained from the image. Therefore, in each cardiac cycle, a pluralityof quality indexes of the images (to be evaluated) of the plurality ofcardiac motion phases may be obtained.

In 6314, a phase of interest may be determined based on the plurality ofquality indexes of the images to be evaluated, and/or one or more targetimages of the phase of interest may be reconstructed.

In some embodiments, the processing device 140 (e.g., the imagereconstruction module 13600) may perform operation 6314.

Specifically, in some embodiments, in each cardiac cycle, the image (tobe evaluated) that has the maximum quality index may be selected, andaccordingly, the phase of the image that has the maximum quality indexin the cardiac cycle may be determined as the phase of interest in thecardiac cycle. In some embodiments, an image reconstructed at the phaseof interest may be determined as a target cardiac image of the phase ofinterest (in the cardiac cycle).

In some embodiments, the processing device 140 may select the one ormore target cardiac images of the phase of interest from the pluralityof cardiac images; or reconstruct the one or more target cardiac imagesof the phase of interest based on one or more sub-sets of projectiondata corresponding to the phase of interest.

According to the process for image reconstruction described above, theinterference(s) of cardiac motion may be eliminated from the targetcardiac image(s). The region of interest may be used as the image to beevaluated, and thus, the automatic evaluation may be performed moreaccurately, further improving the accuracy of automatic evaluation, andsaving the time of doctors in parameter selection for image qualityevaluation.

It should be noted that the above description of process 600 is merelyprovided for the purposes of illustration, and not intended to limit thescope of the present disclosure. For persons having ordinary skills inthe art, multiple variations and modifications may be made under theteachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, operation 6302 may be decomposed into two operations, inwhich a first operation may obtain projection data, and a secondoperation may reconstruct the plurality of initial images. As anotherexample, operation 6306 may be decomposed into two operations, in whicha first operation may select candidate cardiac motion phases, and asecond operation may obtain image(s) the region of interest. As afurther example, operation 6314 may be decomposed into two operations,in which a first operation may determine a phase of interest, and asecond operation may reconstruct target image(s) of the phase ofinterest.

FIG. 7 is a flowchart illustrating an exemplary process for determininga mean phase according to some embodiments of the present disclosure. Insome embodiments, the process 700 may be executed by the imaging system100. For example, the process 700 may be implemented as a set ofinstructions (e.g., an application) stored in one or more storagedevices (e.g., the storage device 150, the storage 220, and/or thestorage 390) and invoked and/or executed by the processing device 140(implemented on, for example, the processor 210 of the computing device200, and the CPU 340 of the mobile device 300). The operations of theprocess 700 presented below are intended to be illustrative. In someembodiments, the process 700 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 700 as illustrated in FIG. 7 and described below is notintended to be limiting. In some embodiments, operation 6304 of FIG. 6may be performed according to one or more operations of the process 700in FIG. 7.

In some embodiments, as shown in FIG. 7, an exemplary process fordetermining a mean phase is provided. The process 700 may include one ormore of the following operations:

In 7402, a plurality of mean absolute differences (MADs) may be obtainedby determining an MAD between two images of each two adjacent cardiacmotion phases. In some embodiments, an MAD between two images of eachtwo adjacent cardiac motion phases may be determined based on the valuesof the elements of the two images and/or the sizes of the two images.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 7402.

Specifically, in some embodiments, in cardiac image reconstruction, thecardia motion phases may range from 1% to 100%. In some embodiments, ifimage evaluation is performed on images of all phases, the imageevaluation efficiency may be relatively low. Therefore, it may bedesirable to set a phase range in which image evaluation may beperformed. In some embodiments, it may be required that the bloodvessel(s) in the three-dimensional image(s) (e.g., obtained bymulti-planar reconstruction) are continuous. If an excessive phase rangeis set, the target region(s) in a series of target images of the phaseof interests in different cardiac cycles may be discontinuous.

In some embodiments, the mean phase may be determined according toclinical experience values. For example, the mean phase in the systolicperiod may be set as 45%, and the mean phase in the diastolic period maybe set as 75%.

In some embodiments, the mean phase may be determined according to theimages. In some embodiments, before determining the mean absolutedifferences, the images corresponding to the plurality of cardiac motionphases may be pre-processed. In some embodiments, the preprocessing mayinclude: performing image segmentation on the images of the plurality ofcardiac motion phases according to one or more thresholds; and removingone or more regions that are unrelated to cardiac motion to obtainimages of one or more regions relating to cardiac motion.

In some embodiments, the plurality of cardiac motion phases may besampled cardiac motion phases. More descriptions of the sampled cardiacmotion phases may be found in Chinese Patent Application No.201811133622.6 entitled “METHODS, SYSTEMS, COMPUTING DEVICES, ANDREADABLE STORAGE MEDIA FOR CARDIAC IMAGE RECONSTRUCTION,” filed on Sep.27, 2018, and Chinese Patent Application No. 201811133609.0 entitled“METHODS, SYSTEMS, COMPUTING DEVICES, AND READABLE STORAGE MEDIA FORCARDIAC IMAGE RECONSTRUCTION,” filed on Sep. 27, 2018, and U.S.application Ser. No. 16/437,003, entitled “SYSTEMS AND METHODS FORRECONSTRUCTING CARDIAC IMAGES,” filed on Jun. 11, 2019, the contents ofwhich are hereby incorporated by reference.

In some embodiments, the MAD between the two images of the each twoadjacent cardiac motion phases may be determined as:

$\begin{matrix}{{{{MAD}\left( {A,B} \right)} = {\frac{1}{{matrix}.^{⩓}2}{\sum_{i}^{matrix}{\sum_{j}^{matrix}{{{A\left( {i,j} \right)} - {B\left( {i,j} \right)}}}}}}},} & (1)\end{matrix}$where A and B represent the (cardiac) images of the each two adjacentcardiac motion phases, respectively; A(i, j) is the gray level of apixel with a coordinate (i, j) in the image A; B(i, j) is the gray levelof a pixel with a coordinate (i, j) in the image B; matrix is the sizeof the image matrix A and/or B; MAD(A, B) is the mean absolutedifference between images A and B.

In 7404, the plurality of cardiac motion parameters corresponding to theplurality of cardiac motion phases may be determined based on theplurality of MADs.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 7404.

Specifically, in some embodiments, an MAD between an image of a cardiacmotion phase and another image of a previous cardiac motion phase may beobtained as a first parameter. In some embodiments, an MAD between animage of a cardiac motion phase and an image of a next cardiac motionphase may be obtained as a second parameter. In some embodiments, thefirst parameter and the second parameter of the same image may be addedto obtain a cardiac motion parameter of the cardiac motion phase.

In some embodiments, the processing device 140 may determine a first MADbetween a first cardiac image of a first cardiac motion phase thatoccurs before the cardiac motion phase and a cardiac image of thecardiac motion phase. In some embodiments, the processing device 140 maydetermine a second MAD between a second cardiac image of a secondcardiac motion phase that occurs after the cardiac motion phase and thecardiac image of the cardiac motion phase. In some embodiments, theprocessing device 140 may further designate a sum of the first MAD andthe second MAD as the cardiac motion parameter corresponding to thecardiac motion phase. In some embodiments, the first cardiac motionphase may be adjacent to the cardiac motion phase. In some embodiments,the second cardiac motion phase may be adjacent to the cardiac motionphase. In some embodiments, the cardiac motion phases (in a same cycleor different cycles) may be arranged based on their respective sequencenumbers, e.g., in an ascending order. The sequence number of a cardiacmotion phase may be determined based on the timing of the cardiac motionphase in the cycle in which the cardiac motion phase occurs relative toa reference time point of the cycle. Exemplary reference time points ofa cycle of the cardiac motion may include the beginning of the cardiaccycle (e.g., the time of contraction of the atria), the end of thecardiac cycle (e.g., the time of ventricular relaxation), or a midpointof the cardiac cycle (e.g., the beginning of the ventricular systole).Cardiac motion phases that occur in different cycles of cardiac motionmay have a same sequence number. If a sequence number of a cardiacmotion phase A is lower than a sequence number of a cardiac motion phaseB, then the cardiac motion phase A may be considered “occur before” thecardiac motion phase B, and accordingly, the cardiac motion phase B may“occur after” the cardiac motion phase A. If the absolute value of adifference between sequence numbers of two cardiac motion phases C and Dis 1, then the cardiac motion phase C and the cardiac motion phase D maybe considered “adjacent to” each other.

In some embodiments, the determination of the cardiac motion parameterof a cardiac motion phase may be represented as:ΔM(P _(l) ,k)=MAD(V _(k)(P _(l) ,i,j))V _(k)(P _(l−1) ,i,j),+MAD(V_(k)(P _(l) ,i,j),V _(k)(P _(l+1) ,i,j)),  (2)where MAD(V_(k)(P_(l),i,j),V_(k)(P_(l−1),i,j)) is the mean absolutedifference between a cardiac image V_(k)(P_(l),i,j) of a current cardiacmotion phase and a cardiac image V_(k)(P_(l−1),i,j) of a cardiac motionphase that occurs before the current cardiac motion phase;MAD(V_(k)(P_(l),i,j),V_(k)(P_(l+1),i,j)) is the mean absolute differencebetween the cardiac image V_(k)(P_(l),i,j) of the current cardiac motionphase and a cardiac image V_(k)(P_(l+1),i,j) of a cardiac motion phasethat occurs after the current cardiac motion phase; ΔM(P_(l),k) is thecardiac motion parameter corresponding to the cardiac image of thecurrent cardiac motion phase.

In Equation (2), P_(l) is the current cardiac motion phase, l is asequence number of the current cardiac motion phase in the plurality ofcardiac motion phases, k is a sequence number of a slice of the object,i and j represent the element locations in a corresponding cardiacimage. In some embodiments, the number (or count) of the cardiac motionparameters (e.g., the ΔM(P_(l),k) in Equation (2)) may be less than thenumber (or count) of the cardiac motion phases.

In 7406, a mean phase may be determined based on the plurality ofcardiac motion parameters corresponding to the plurality of cardiacmotion phases.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 7406. In some embodiments,a first mean phase in the systolic period and/or a second mean phase inthe diastolic period may be determined.

Specifically, in some embodiments, in a systolic period of cardiacmotion, a cardiac motion phase corresponding to a minimum cardiac motionparameter in the systolic period may be designated as the mean phase inthe systolic period. In a diastolic period of cardiac motion, a cardiacmotion phase corresponding to a minimum cardiac motion parameter in thediastolic period may be designated as the mean phase in the diastolicperiod.

In some embodiments, the determination of the mean phase in the systolicperiod may be represented as:P _(Basic)1=arg_(l) min(Σ_(k) ^(N) ΔM(P _(l) ,k)/N),for allP _(l) whereP _(1S) ≤P _(l) ≤P _(1E),  (3)where P_(Basic)1 is the mean phase in the systolic period; N is thenumber (or count) of cardiac images of the cardiac motion phases in thesystolic period; (P_(1S)≤P_(l)≤P_(1E)) is the range of the cardiacmotion phases in the systolic period.

In Equation (3), P_(1E) is the end phase in the systolic period, andP_(1S) is the start phase in the systolic period.

In an embodiment, the determination of the mean phase in the diastolicperiod may be represented as:P _(Basic)2=arg_(l) min(Σ_(k) ^(N) ΔM(P _(l) ,k)/N),for allP _(l) whereP _(2S) ≤P _(l) ≤P _(2E),  (4)where P_(Basic)2 is the mean phase in the diastolic period; N is thenumber (or count) of cardiac images of the cardiac motion phases in thediastolic period; (P_(2S)≤P_(l)≤P_(2E)) is the range of cardiac motionphases in the diastolic period.

In Equation (4), P_(2E) is the end phase in the diastolic period, P_(2S)is the start phase in the diastolic period.

According to the process for determining the mean phase described above,the cardiac motion parameters of the corresponding cardiac motion phasesmay be determined based on the mean absolute differences between eachtwo cardiac images of each two adjacent cardiac motion phases, and thecardiac motion phase with the minimum (or maximum) cardiac motionparameter may be designated as the mean phase. Therefore, the mean phasemay be determined accurately, and the accuracy of the determination ofthe optimal phase in the cardiac motion may be ensured.

FIG. 8 is a flowchart illustrating an exemplary process for extractingan image of a region of interest according to some embodiments of thepresent disclosure. In some embodiments, the process 800 may be executedby the imaging system 100. For example, the process 800 may beimplemented as a set of instructions (e.g., an application) stored inone or more storage devices (e.g., the storage device 150, the storage220, and/or the storage 390) and invoked and/or executed by theprocessing device 140 (implemented on, for example, the processor 210 ofthe computing device 200, and the CPU 340 of the mobile device 300). Theoperations of the process 800 presented below are intended to beillustrative. In some embodiments, the process 800 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 800 as illustrated in FIG. 8 and describedbelow is not intended to be limiting. In some embodiments, operation6306 of FIG. 6 may be performed according to one or more operations ofthe process 800 in FIG. 8.

In some embodiments, as shown in FIG. 8, an exemplary process forextracting an image of a region of interest is provided. The process 800may include one or more of the following operations:

In 8502, initial images of the candidate cardiac motion phases may beselected in a preset phase range including the mean phase.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 8502. More descriptions ofthe preset phase range may be found elsewhere in the present disclosure(e.g., FIG. 5 and descriptions thereof).

Specifically, in some embodiments, the preset phase range may becentered at the mean phase, and may be obtained by extending the meanphase for a certain number of phases. If the preset phase range is toosmall, the phase of interest may be not included in the preset range. Ifthe phase range is too large, the target region(s) in a series of targetimages of the phase of interests in different cardiac cycles may bediscontinuous. Therefore, the setting of the preset range may beimportant. In some embodiments, the preset range may be centered at themean phase, with 10% forward extension and 10% backward extension.

In some embodiments, a first preset range may be determined based on thefirst mean phase in the systolic period, and/or a second preset rangemay be determined based on the second mean phase in the diastolicperiod. The first preset range associated with the systolic period mayalso be referred to as a systolic preset range. The second preset rangeassociated with the diastolic period may also be referred to as adiastolic preset range.

In 8504, the selected initial images may be smoothed using a low passfilter.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 8504.

Specifically, in some embodiments, the selected initial images withinthe preset range may be smoothed using a Gaussian low-pass filter. TheGaussian low-pass filter may eliminate the effects of noise and producessmoothed images for subsequent image processing.

In 8506, one or more ventricular images may be extracted from thesmoothed images.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 8506. In some embodiments,the processing device 140 may extract a ventricular image based on eachsmoothed image.

Specifically, in some embodiments, image segmentation may be performedaccording to the smoothed image(s) and a threshold associated withbone(s). In some embodiments, elements of a smoothed image that havegray levels higher than the threshold associated with bone(s) may beextracted as a bone image. In some embodiments, one or more bone imagesmay be obtained similarly based on the smoothed images. In someembodiments, a maximum intensity projection may be performed on the boneimage(s) in an axial direction of the thoracic cavity, and a maximumintensity projection image of the bone image(s) may be obtained. Themaximum intensity projection may be generated based on element(s) havinga maximum intensity (or density) along each projection ray directed tothe object's target site. That is, if the projection ray passes throughthe smoothed images, the element(s) with the highest intensity (ordensity) in the image(s) may be retained and projected onto atwo-dimensional plane, thereby forming a maximum intensity projectionimage of the bone image(s). According to the maximum intensityprojection image of the bone image(s), the maximum intensity projectionimage (e.g., elements of the maximum intensity projection image) of thebone image(s) may correspond to different Boolean values. A thoraciccontour boundary may be determined according to boundaries of thedifferent Boolean values. In some embodiments, a pleural image may beobtained based on the smoothed images and the thoracic contour boundary.In some embodiments, elements within the thoracic contour boundary maybe extracted from the smoothed images to obtain a pleural image (orthoracic contour image). Then, connected domain(s) may be determinedbased on a pleural image; a target connected domain with a maximumnumber of elements among the connected domain(s) may be extracted as aventricular image. A connected domain may correspond to a region in acomplex plane. If a simple closed curve is used in the complex plane,and the internal of the closed curve always belongs to the region, thenthe region is a connected domain.

More descriptions of the extraction of the ventricular images may befound elsewhere in the present disclosure (e.g., FIG. 9 and descriptionsthereof).

In 8508, a threshold associated with gray level(s) of a contract agentmay be determined based on the ventricular image(s).

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 8508.

Specifically, in some embodiments, the extraction of the image(s) of theregion of interest may be performed based on image segmentation usingthe threshold associated with gray level(s) of the contrast agent. TheCT values of different concentrations of contrast agents are different,and the segmentation of the region(s) including the contrast agent maynot be performed based on an empirical threshold. Therefore, it isnecessary to determine the threshold associated with the contrast agentaccording to the current obtained image(s). In some embodiments,gradient image(s) of the ventricular image(s) may be determined based onthe ventricular image(s). In image processing, modulus (or moduli) ofgradient(s) may be simply referred to as gradient(s), and an image usingthe gradient(s) as elements may be referred to as a gradient image. Ifan image (e.g., a ventricular image) includes an edge (e.g., ofdifferent portions of an object), a corresponding gradient image mayinclude relatively large gradient value(s). If the image includes arelatively smooth part, and difference(s) between gray level(s) arerelatively low, then the corresponding gradient value(s) may berelatively low. In some embodiments, the determination of the gradientimage(s) may be performed using the Sobel operator. The Sobel operatoris a discrete first-order difference operator used to determine anapproximation of a first-order gradient of an image brightness function.A gradient vector corresponding to an element of an image may begenerated by applying the Sobel operator to the element in the image. Insome embodiments, the gray level(s) of the element(s) in the gradientimage may be analyzed statistically, and a target ventricular imagewhose corresponding gradient image has elements with values larger thana proportional threshold may be determined as a marker image. In someembodiments, the gray level(s) of the element(s) in the gradient imagemay be analyzed statistically to obtain a histogram of the element(s);an appropriate proportion of gray level(s) may be selected as aproportional threshold; and the element(s) with gray level(s) greaterthan the proportional threshold may be extracted to obtain a markerimage. In some embodiments, the threshold associated with the contrastagent may be determined based on the value(s) of the element(s) of themarker image using the OTSU algorithm. The OTSU algorithm is anefficient algorithm for the binarization of image(s), using a thresholdto segment an original image into a foreground image and a backgroundimage. An optimal segmentation threshold may be taken as the thresholdassociated with the gray level(s) of the contrast agent.

In 8510, one or more contrast agent images may be obtained by segmentingthe ventricular image(s) based on the threshold.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 8510. In some embodiments,the processing device 140 may obtain a contrast agent image bysegmenting each ventricular image based on the threshold.

Specifically, in some embodiments, the image segmentation may beperformed based on the threshold associated with the contrast agent.Element(s) with gray level(s) greater than the threshold associated withthe contrast agent may be extracted from the ventricular image(s) toobtain the contrast agent image(s).

In 8512, image(s) of the region of interest (or vascular image(s) ofinterest) may be extracted based on the contrast agent image(s).

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 8512.

Specifically, in some embodiments, the right coronary artery is anarterial blood vessel that is clinically more visible than other bloodvessels, the motion of the right coronary artery may reflect the motionof the heart, and then the motion of the heart in different phases maybe determined by detecting the motion of the right coronary artery inthe corresponding phases. In some embodiments, in the contrast agentimage(s), image(s) having a relatively low amount of elements associatedwith the contrast agent and having elements with relatively lowextravascular CT values may be extracted from portion(s) of the contrastagent image(s) corresponding to the upper left half of the ventricle toobtain the vascular image(s) of interest.

According to the process for extracting the vascular image(s) ofinterest described above, ventricular image(s) may be extracted from thesmoothed images; the threshold associated with gray level(s) of thecontrast agent may be determined according to the ventricular image(s);the ventricular image(s) may be segmented based on the thresholdassociated with the contrast agent to obtain the contrast agentimage(s); and the vascular image(s) of interest may be extracted fromthe contrast agent image(s). Therefore, right coronary vascular image(s)may be determined accurately in the images of the preset phase range,thereby improving the accuracy of the determination of the optimal phase(or phase of interest) in the cardiac motion.

FIG. 9 is a flowchart illustrating an exemplary process for extracting aventricular image according to some embodiments of the presentdisclosure. In some embodiments, the process 900 may be executed by theimaging system 100. For example, the process 900 may be implemented as aset of instructions (e.g., an application) stored in one or more storagedevices (e.g., the storage device 150, the storage 220, and/or thestorage 390) and invoked and/or executed by the processing device 140(implemented on, for example, the processor 210 of the computing device200, and the CPU 340 of the mobile device 300). The operations of theprocess 900 presented below are intended to be illustrative. In someembodiments, the process 900 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 900 as illustrated in FIG. 9 and described below is notintended to be limiting. In some embodiments, operation 8506 of FIG. 8may be performed according to one or more operations of the process 900in FIG. 9.

In some embodiments, as shown in FIG. 9, an exemplary process forextracting ventricular image(s) is provided. The process 900 may includeone or more of the following operations:

In 9602, one or more bone images may be extracted from the smoothedimages and based on a threshold associated with gray level(s) ofbone(s).

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 9602.

Specifically, in some embodiments, according to the threshold associatedwith the bone(s), bone image(s) that have elements with gray level(s)larger than the threshold associated with the bone(s) may be extracted.The threshold associated with the bone(s) in the thoracic cavity maygenerally be around 1500 HU according to the clinical experience. Thatis, region(s) including elements with gray level(s) greater than 1500 HUmay be extracted from the smoothed images and may be regarded as thebone image(s).

In 9604, a maximum intensity projection image of the one or more boneimages may be obtained by performing a maximum intensity projection onthe one or more bone images.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 9604.

Specifically, in some embodiments, the maximum intensity projectionimage may be generated based on element(s) having a maximum intensity(or density) along each projection ray directed to the target site ofthe object. That is, if the projection ray passes through the smoothedimages, the element(s) with the highest intensity (or density) in theimage(s) may be retained and projected onto a two-dimensional plane,thereby forming a maximum intensity projection image of the boneimage(s).

In 9606, a thoracic contour boundary may be determined for the maximumintensity projection image.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 9606.

Specifically, in some embodiments, according to the maximum intensityprojection image of the bone image(s), the Boolean value of elements ina ventricular region of the maximum intensity projection image of thebone image(s) may be set as 1, and the Boolean value of elements in thenon-ventricular region of the maximum intensity projection image of thebone image(s) may be set as 0. A boundary of elements with Boolean value1 and elements with Boolean value 0 may be taken as a thoracic contourboundary.

In some embodiments, a thoracic contour boundary may correspond to abinary image, wherein elements inside the thoracic contour boundary mayhave the Boolean value 1, while elements outside the thoracic contourboundary may have the Boolean value 0. In some embodiments, the thoraciccontour boundary may include one or more elements representing one ormore positions of a thoracic contour boundary of an object.

In 9608, one or more ventricular images may be obtained based on thethoracic contour boundary and/or the smoothed images.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 9608.

Specifically, in some embodiments, a pleural image may be obtainedaccording to the smoothed images and the thoracic contour boundary.Connected domain(s) may be determined according to the pleural image,and a target connected domain with a maximum number of elements amongthe connected domain(s) may be identified as a ventricular (mask) image.

According to the smoothed images and the thoracic contour boundary, apleural image may be obtained. A region within a thoracic contourboundary may be extracted as a pleural image in one of the smoothedimages. That is, a region that has specific elements may be extracted inthe smoothed images as a pleural image. The specific elements may havevalue(s) larger than a threshold associated with soft tissue(s) and mayhave a Boolean value 1.

Connected domain(s) may be determined according to the pleural image,and a target connected domain with a maximum number of elements amongthe connected domain(s) may be designated as a ventricular (mask) image.Based on the pleural image, the target connected domain with the maximumnumber of elements may be designated as the ventricular (mask) image. Aconnected domain may correspond to a region in a complex plane. If asimple closed curve is used in the complex plane, and the internal ofthe closed curve always belongs to the region, then the region is aconnected domain.

In some embodiments, the ventricular image(s) may be obtained based onthe ventricular mask image and the smoothed images.

According to the process for extracting the ventricular image(s), imagesegmentation may be performed on the smoothed image(s) based on athreshold associated with bone(s) to obtain bone image(s); a maximumintensity projection may be performed on the bone image(s) to obtain amaximum intensity projection image. The process may further includedetermining a thoracic contour boundary according to the maximumintensity projection image, extracting a region within the thoraciccontour boundary as a pleural image, determining connected domain(s) ofthe pleural image, and designating a target connected domain with themaximum number of elements as the ventricular (mask) image. Therefore,the thoracic contour boundary may be accurately determined, therebyimproving the accuracy of the determination of the ventricular image(s),and improving the accuracy of the determination of the heart region.

FIG. 10 is a flowchart illustrating an exemplary process for extractinga blood vessel centerline associated with image(s) of a region ofinterest according to some embodiments of the present disclosure. Insome embodiments, the process 1000 may be executed by the imaging system100. For example, the process 1000 may be implemented as a set ofinstructions (e.g., an application) stored in one or more storagedevices (e.g., the storage device 150, the storage 220, and/or thestorage 390) and invoked and/or executed by the processing device 140(implemented on, for example, the processor 210 of the computing device200, and the CPU 340 of the mobile device 300). The operations of theprocess 1000 presented below are intended to be illustrative. In someembodiments, the process 1000 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 1000 as illustrated in FIG. 10 and described below is notintended to be limiting. In some embodiments, operation 6308 of FIG. 6may be performed according to one or more operations of the process 1000in FIG. 10.

In some embodiments, as shown in FIG. 10, an exemplary process forextracting a blood vessel centerline is provided. The process 1000 mayinclude one or more of the following operations:

In 10702, one or more images in a coronal plane and/or one or moreimages in a sagittal plane may be generated based on one or more imagesof the region of interest (e.g., the image(s) of the region of interestobtained in operation 6306 of process 600).

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 10702.

Specifically, in some embodiments, the image(s) of the region ofinterest (obtained in 6306) may have one or more connected domains. Insome embodiments, the connected domain(s) may include a branch of ablood vessel that does exist in a current transverse layer, and/or oneor more other connected domains of non-vascular regions. Exemplaryconnected domains of non-vascular regions like calcification, metal(s),and/or bone(s) may be classified into regions of interest. Therefore,for subsequent image processing, false positive vessel(s) may need to beexcluded, and the blood vessel centerline may be extracted. In someembodiments, the blood vessels are continuous in both the coronal planeand the sagittal plane. Therefore, the image(s) in the coronal plane andimage(s) in the sagittal plane may be generated first to further filterout false positive vessel(s). The image(s) in the coronal plane andimage(s) in the sagittal plane may be obtained based on the image(s) ofthe region of interest. The coronal plane may also be referred to as thefrontal plane, that is, along the left and right direction of the objectand along the longitude axis of the object, the object may belongitudinally segmented into the anterior and posterior sections. Thesagittal plane may be an anatomical plane that segments the object intoleft and right sections.

In some embodiments, each image of the region of interest may be animage in a transverse plane. In some embodiments, volume data(corresponding to a plurality of voxels) may be generated based on theimages of the region of interest. In some embodiments, the volume datamay be generated by performing a three-dimensional reconstructiontechnique based on the images of the region of interest, andaccordingly, the image(s) in the coronal plane and the image(s) in thesagittal plane may be obtained based on the volume data. Thethree-dimensional reconstruction technique may include multiplanarreconstruction (MPR). The MPR may stack a plurality of axial images(e.g., traverse images) within the scanning scope, and then performimage reformation on a specified tissue or in a specified scope in thecoronal plane, the sagittal plane or an oblique plane with any angle, sothat a new slice image in coronal plane, sagittal plane or oblique planewith any angle may be generated.

Alternatively, the image(s) of the region of interest may be image(s) inthe coronal plane (or the sagittal plane), and the images in otherplanes (e.g., the sagittal plane, the transverse plane, or the like) maybe generated according to the three-dimensional reconstruction technique(e.g., MPR) similarly.

In 10704, a blood vessel main body may be determined based on the one ormore images in the coronal plane and/or the one or more images in thesagittal plane.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 10704.

Specifically, in some embodiments, the blood vessel main body may belocated in the middle part of the image(s) of the region of interest (orthe volume data) and/or in the largest connected domain of the image(s)of the region of interest (or the volume data). In some embodiments, theblood vessel main body may be determined based on the image(s) in thecoronal plane and/or the image(s) in the sagittal plane.

In 10706, one or more false positive vessels may be filtered out fromthe blood vessel main body.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 10706.

Specifically, in some embodiments, according to the blood vessel mainbody (also referred to as a main blood vessel), the non-main bloodvessels may be filtered out, and the main blood vessel(s) may be furtherfiltered according to the blood vessel main body after filtering thenon-main blood vessels. False positive vessels may include or refer tonon-vascular regions. In some embodiments, a true positive blood vesselmay satisfy one or more criteria. A first criterion may relate tosmoothness, which means the distance between a candidate blood vessel(or a blood vessel to be determined) and the already determined vesselin the X axis direction may not be too large, and/or the distancebetween the candidate blood vessel and the determined blood vessel inthe transverse plane may not be too large. The position of the candidateblood vessel may refer to the position of the maximum value of theconnected domain of the non-main region. The position of the determinedblood vessel may refer to the maximum value of the main blood vesselclosest to the candidate blood vessel. A second criterion may relate tocontinuity, which means the distance of the candidate blood vessel andthe determined blood vessel in the Y axis direction (or the Z axisdirection) may not be too large (e.g., may be no large than athreshold). In some embodiments, the distance of the candidate bloodvessel and the determined blood vessel in the axial direction of thedetermined blood vessel may not be too large (e.g., may be no large thana threshold). In some embodiments, the distance of the candidate bloodvessel and the determined blood vessel in the radial direction of thedetermined blood vessel may not be too large (e.g., may be no large thana threshold). If a connected domain is not detected in a transverselayer, the distance may increase by 1. If a connected domain is notdetected in a plurality of transverse layers (e.g., the distance exceedsa threshold), it may be determined that the continuity criterion is notsatisfied. In some embodiments, if the candidate blood vessel satisfiesthe smoothness criterion and continuity criterion simultaneously, thecandidate blood vessel may be determined as a determined blood vessel.In some embodiments, the mean value of the positions of all thedetermined blood vessels in the X axis direction may be determined. Insome embodiments, the candidate blood vessel closest to the mean valuemay be determined as an effective blood vessel. In some embodiments, themean value of the positions of all the determined blood vessels amongthe main blood vessels in the X axis direction may be determined. Insome embodiments, the candidate blood vessel closest to the mean valuemay be determined as an effective blood vessel.

In some embodiments, after filtering the non-main blood vessels, themain blood vessel(s) may be further filtered to identify an effectiveblood vessel among one or more main blood vessels. In some embodiments,for the blood vessels near the two ends of the blood vessel main body,the mean value of the positions of all the determined blood vessels inthe X axis direction may be determined, and the candidate blood vesselclosest to the mean value may be determined as an effective bloodvessel. In some embodiments, for the blood vessels near a middle segmentof the blood vessel main body, the mean value of the positions of allthe determined blood vessels among the main blood vessels in the X axisdirection may be determined, and the candidate blood vessel closest tothe mean value may be determined as an effective blood vessel.

In 10708, a blood vessel center may be identified in each transverselayer associated with the image(s) of the region of interest based onthe filtered blood vessel main body.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 10708.

Specifically, in some embodiments, the blood vessel centers may bedetermined using interpolation according to the main blood vesselobtained after filtering out the false positive vessels. In someembodiments, the position of the blood vessel center in each transverselayer may be determined according to the positions of the blood vesselcenters in the sagittal plane and the coronal plane.

In 10710, a blood vessel centerline associated with the image(s) of theregion of interest may be generated based on the blood vessel center inthe each transverse layer.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 10710.

According to the process for extracting the blood vessel centerlinecorresponding to the image(s) of the region of interest, image(s) in acoronal plane and/or image(s) in a sagittal plane may be generated basedon the image(s) of the region of interest; a blood vessel main body maybe determined; one or more false positive vessels may be filtered outfrom the blood vessel main body; a blood vessel center may be identifiedin each transverse layer associated with the image(s) of the region ofinterest; a blood vessel centerline associated with the image(s) of theregion of interest may be generated. Therefore, the image(s) of theregion of interest may be accurately determined, thereby improving theaccuracy of the determination of the heart region.

FIG. 11 is a flowchart illustrating an exemplary process for determiningimage(s) to be evaluated according to some embodiments of the presentdisclosure. In some embodiments, the process 1100 may be executed by theimaging system 100. For example, the process 1100 may be implemented asa set of instructions (e.g., an application) stored in one or morestorage devices (e.g., the storage device 150, the storage 220, and/orthe storage 390) and invoked and/or executed by the processing device140 (implemented on, for example, the processor 210 of the computingdevice 200, and the CPU 340 of the mobile device 300). The operations ofthe process 1100 presented below are intended to be illustrative. Insome embodiments, the process 1100 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 1100 as illustrated in FIG. 11 and described below is notintended to be limiting. In some embodiments, operation 6310 of FIG. 6may be performed according to one or more operations of the process 1100in FIG. 11.

In some embodiments, as shown in FIG. 11, an exemplary process fordetermining image(s) to be evaluated is provided. The process 1100 mayinclude one or more of the following operations:

In 11802, a transformed image of the region of interest may be generatedby performing a top-hat transformation on each image of the region ofinterest.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 11802.

Specifically, the top-hat transformation is an image processingalgorithm that may weaken a background in an image and make a targetobject more prominent. That is, the top-hat transformation of the imageof the region of interest may make a target region in the image of theregion of interest more prominent. In some embodiments, the targetobject may include a blood vessel. In some embodiments, after thetop-hat transformation of the image of the region of interest, thebackground (in the image of the region of interest) may be weakened andthe blood vessel(s) (in the image of the region of interest) may beshown more clearly.

In 11804, an image including the intraventricular region may begenerated by segmenting, based on a threshold associated with graylevel(s) of soft tissue(s), the transformed image of the region ofinterest.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 11804.

Specifically, in some embodiments, an empirical threshold associatedwith the gray level(s) of the soft tissue(s) may be 800 HU. According tothe threshold associated with the gray level(s) of soft tissue(s), thetransformed image of region of interest may be segmented to obtain animage that reserves the intraventricular region. The image that reservesthe intraventricular region may be also referred to as an imageincluding the intraventricular region.

In 11806, an image to be evaluated may be obtained by extracting, basedon a preset region including the blood vessel centerline, elements ofthe image including the intraventricular region.

In some embodiments, the processing device 140 (e.g., the imageselection module 13400) may perform operation 11806.

Specifically, in some embodiments, an image (to be evaluated) of acorresponding cardiac motion phase may be obtained by extracting, basedon a preset region including the blood vessel centerline, elements ofthe image including the intraventricular region. In some embodiments,each transverse layer may have a corresponding preset region. In someembodiments, a preset region of a transverse layer may be centered at ablood vessel center in the transverse layer and may have a size of N*N.In some embodiments, the pixels in the preset region may be extracted asthe image to be evaluated in the transverse layer. In some embodiments,N may refer to a physical size within 50-100 mm. In some embodiments,before segmentation, it may be necessary to determine whether N exceedsthe boundary of the current image including the intraventricular region.If N exceeds the boundary of the current image, it may be necessary topad the image before segmentation. The padded value may be 0 or thevalue(s) of the element(s) of the boundary. In some embodiments, theimages to be evaluated may be extracted from the images of phases withinthe preset range (e.g., 10%) around the mean phase, and the imagematrixes to be evaluated in the systolic preset range and/or the imagematrixes to be evaluated in the diastolic preset range may be obtained.

According to the process for extracting the images to be evaluated, therange of the images to be evaluated may be determined accuratelyaccording to the blood vessel centerline, and thereby improving theaccuracy of the determination of the phase of interest.

FIG. 12 is a flowchart illustrating an exemplary process for determininga quality index of an image to be evaluated according to someembodiments of the present disclosure. In some embodiments, the process1200 may be executed by the imaging system 100. For example, the process1200 may be implemented as a set of instructions (e.g., an application)stored in one or more storage devices (e.g., the storage device 150, thestorage 220, and/or the storage 390) and invoked and/or executed by theprocessing device 140 (implemented on, for example, the processor 210 ofthe computing device 200, and the CPU 340 of the mobile device 300). Theoperations of the process 1200 presented below are intended to beillustrative. In some embodiments, the process 1200 may be accomplishedwith one or more additional operations not described, and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 1200 as illustrated in FIG. 12 anddescribed below is not intended to be limiting. In some embodiments,operation 5204 of FIG. 5 may be performed according to one or moreoperations of the process 1200 in FIG. 12.

In 12902, one or more sub-images of a region of interest may bedetermined by segmenting, based on one or more thresholds, an image tobe evaluated.

In some embodiments, the processing device 140 (e.g., the quality indexdetermination module 13500) may perform operation 12902. In someembodiments, operation 12902 may be similar to operation 4104. The imageto be evaluated in 12902 may be an image obtained in 4102, an imageobtained in 11806, etc. In some embodiments, the region of interest mayinclude blood vessel(s). In some embodiments, each sub-image of theregion of interest may refer to a vascular image of interest.

Specifically, in some embodiments, before determining the quality index,the resolution of the image to be evaluated may be improved. Improvementof the resolution(s) may cause the improvement of the accuracy ofdetermination of the blood vessel morphologies and/or the blood vesselboundaries. In some embodiments, the resolution(s) of the image(s) maybe improved using a two-dimensional image interpolation algorithm. Insome embodiments, the maximum gray level of the image to be evaluatedmay be selected. In some embodiments, the maximum gray level multipliedby one or more predetermined multiples may be designated as one or morethresholds for segmenting the images to be evaluated. In someembodiments, elements of the image (to be evaluated) with gray level(s)larger than (and/or equal to) a segmentation threshold may be extractedas a vascular image of interest corresponding to the segmentationthreshold. In some embodiments, two or more vascular images of interestmay be obtained by segmenting the image based on two or moresegmentation thresholds. For example, three segmentation thresholds maybe obtained according to the maximum gray level of the image multipliedby three predetermined multiples. In some embodiments, the image may besegmented based on a first segmentation threshold of the threesegmentation thresholds. For example, a region of the image havingelements with gray levels of greater than the first segmentationthreshold may be designated as a first vascular image of interest. Insome embodiments, the image may be segmented based on a secondsegmentation threshold of the three segmentation thresholds. Forexample, a region of the image having elements with gray levels greaterthan the second segmentation threshold may be designated as a secondvascular image of interest. In some embodiments, the image may besegmented based on a third segmentation threshold of the threesegmentation thresholds. For example, a region of the image havingelements with gray levels greater than the third segmentation thresholdmay be designated as a third vascular image of interest.

In 12904, a perimeter and/or an area of a target region in the image tobe evaluated may be determined based on the sub-images of the region ofinterest.

In some embodiments, the processing device 140 (e.g., the quality indexdetermination module 13500) may perform operation 12904. In someembodiments, the target region in the image may also be referred to asthe region of interest in the image, for example, a region of a bloodvessel.

Specifically, in some embodiments, the perimeter and/or the area of thetarget region in the image to be evaluated may be determined accordingto the sub-images of the region of interest (or vascular images ofinterest). That is, the perimeter and the area of the blood vessel ineach vascular image of interest may be respectively determined.Therefore, one or more perimeters and/or one or more areas of the targetregions in the sub-images of the region of interest may be determined.

In some embodiments, connected domains of the sub-images of the regionof interest may be obtained. In some embodiments, if the number (orcount) of the elements in a connected domain is no less than a firstthreshold, and/or the contrast of the elements in the connected domainis no less than a second threshold, then the connected domain may bedetermined as a target region to be evaluated. Accordingly, theperimeter and/or the area of the target region (e.g., the qualified oridentified connected domains) may be determined.

In 12906, a regularity degree for the image to be evaluated may bedetermined based on the perimeter(s) and the area(s) of the targetregion in the image.

In some embodiments, the processing device 140 (e.g., the quality indexdetermination module 13500) may perform operation 12906. Moredescriptions of the determination of the regularity degree may be foundin Chinese Patent Application No. 201811133622.6 entitled “METHODS,SYSTEMS, COMPUTING DEVICES, AND READABLE STORAGE MEDIA FOR CARDIAC IMAGERECONSTRUCTION,” filed on Sep. 27, 2018, and Chinese Patent ApplicationNo. 201811133609.0 entitled “METHODS, SYSTEMS, COMPUTING DEVICES, ANDREADABLE STORAGE MEDIA FOR CARDIAC IMAGE RECONSTRUCTION,” filed on Sep.27, 2018, and U.S. application Ser. No. 16/437,003, entitled “SYSTEMSAND METHODS FOR RECONSTRUCTING CARDIAC IMAGES,” filed on Jun. 11, 2019,the contents of which are hereby incorporated by reference. For example,the regularity degree may be determined based on a compactness degree.In some embodiments, the compactness degree may reflect a closenessdegree of the element(s) in an image or a region of interest thereof.The compactness degree may relate to a perimeter and/or an area of aregion (e.g., the target object) including a portion or all elements inthe image or a region of interest thereof. In some embodiments, thecompactness degree may be in direct proportion to the perimeter and/orinversely proportional to the area.

In 12908, a sharpness degree of the image may be determined based on thesub-images of the region of interest.

In some embodiments, the processing device 140 (e.g., the quality indexdetermination module 13500) may perform operation 12908. In someembodiments, the sharpness degree of the image may be determinedaccording to the edge(s) of the vascular image(s) of interest, and/orthe gradient map(s) of the vascular image(s) of interest. In someembodiments, the sharpness degree of an image may be determined based ona cross product of the edge of the image and the gradient map of theimage.

In some embodiments, the processing device 140 may identify connecteddomain(s) in the sub-image(s) of the image to be evaluated. In someembodiments, the target object in the image to be evaluated may refer tothe identified connected domain(s) in the sub-image(s) of the image. Insome embodiments, for each of the identified connected domains in theimage, the processing device 140 may determine an edge of the identifiedconnected domain, and/or determine a gradient map of the identifiedconnected domain. Therefore, one or more edges and/or one or moregradient maps may be determined for the image to be evaluated. Further,the processing device 140 may determine, based on the one or more edgesand/or the one or more gradient maps, the sharpness degree of the image.In some embodiments, the processing device 140 may determine the number(or count) of the identified connected domain(s) in the image to beevaluated. In some embodiments, in response to a determination that thecount of the identified connected domain(s) in the image is zero, theprocessing device 140 may designate the regularity degree of the imageas zero, and/or designate the sharpness degree of the image as zero. Insome embodiments, the processing device 140 may adjust, based on thecount of the connected domain(s) in the image, the regularity degree forthe image. In some embodiments, the processing device 140 may adjust,based on the count of the connected domain(s) in the image, thesharpness degree of the region of interest in the image.

In 12910, the quality index for the image may be determined based on theregularity degree and the sharpness degree of the image.

In some embodiments, the processing device 140 (e.g., the quality indexdetermination module 13500) may perform operation 12910. In someembodiments, the processing device 140 may designate a weighted sum ofthe regularity degree and the sharpness degree as the quality index forthe image. In some embodiments, the quality index for each of the imagesto be evaluated obtained in 4102 or 11806 may be determined similarly asillustrated above.

In some embodiments, the number (or count) of target regions (e.g.,blood vessels) in different images may be inconsistent if the images aredetected based on a same target object (e.g., at an identical physicalposition) but at different cardiac motion phases. In some embodiments,the image quality evaluation may need to be performed on the images(e.g., the images obtained in 4102 or 11806) based on a same reference(or criteria), that is, the number (or count) of target regions (e.g.,blood vessels) in the images that are detected based on a same targetobject (e.g., the blood vessels) at an identical physical position mayneed to be consistent. In some embodiments, a reference parameter (e.g.,the number (or count) of basic blood vessels, or the number (or count)of blood vessels of a predetermined basic cardiac motion phase) may beintroduced.

In some embodiments, according to the number (or count) of basic bloodvessels and/or the number (or count) of blood vessels in each of theimages to be evaluated, a regularity degree matrix of the images (to beevaluated) of each cardiac motion phase and a sharpness degree matrix ofthe images (to be evaluated) of each cardiac motion phase may beobtained.

An element of the regularity degree matrix may represent a regularitydegree of a layer image of a cardiac motion phase. An element of thesharpness degree matrix may represent a sharpness degree of a layerimage of a cardiac motion phase. In some embodiments, the number (orcount) of blood vessels in each of the images to be evaluated may becompared with the number (or count) of basic blood vessels, andaccordingly, the determination of the regularity degree matrix and/orthe sharpness degree matrix may be adjusted based on the comparisonresult. For example, if the count of blood vessels in an image is equalto the count of basic blood vessels, and is larger than 1, theregularity degree of the image may be determined based on an averagecompactness degree of the blood vessels. As another example, if thecount of blood vessels in an image is larger than 1 and the count ofbasic blood vessels, and the count of basic blood vessels is larger than0, then a same number of blood vessels as the count of basic bloodvessels may be selected for the determination of the regularity degreeand/or the sharpness degree. As a further example, if the count of thebasic blood vessels is 0, and the count of the blood vessels in theimage is larger than 1, a blood vessel nearest to a center of the imagemay be selected for the determination of the regularity degree and/orthe sharpness degree.

In some embodiments, the magnitudes of the regularity degree(s) and thesharpness degree(s) may be different. Therefore, it is desirable toadjust the regularity degree(s) and the sharpness degree(s) to a samebaseline. In some embodiments, the regularity degree(s) and/or thesharpness degree(s) may be adjusted based on a weighting process, anormalization process, or the like, or a combination thereof. In someembodiments, a quality index matrix for the diastolic period and aquality index matrix for the systolic period may be generated. In someembodiments, a quality index matrix for a specific cardiac cycle may bedetermined. If the number (or count) of the quality indexes withnon-zero values in the quality index matrix is no less than an averagenumber (or count) (e.g., the total number (or count) of the qualityindexes with non-zero values in a plurality of cardiac cycles divided bythe number (or count) of the plurality of cardiac cycles), the qualityindexes of one or more images corresponding to each phase in thespecific cardiac cycle may be averaged to obtain an average value, and aphase corresponding to the maximum average value may be designated asthe phase of interest in the specific cardiac cycle. In someembodiments, if a cardiac cycle has no quality index matrix (e.g., allthe quality indexes in the quality index matrix corresponding to thecardiac cycle are 0), the phase of interest in the cardiac cycle may bedetermined as equal to the phase of interest in an adjacent cardiaccycle of the current cardiac cycle. In some embodiments, a series oftarget images may be determined based on the phases of interest of theplurality of cardiac cycles. In some embodiments, the target image(s) ofthe phase of interest in a cardiac cycle may be extracted from thereconstructed images. In some embodiments, the target image(s) may bedirectly obtained by reconstructing the target image(s) of the phase ofinterest.

In some embodiments, each quality index matrix may correspond to acardiac cycle. A quality index matrix may have a size of [K, Q], inwhich K refers to the number (or count) of layers for cardiacreconstruction, Q refers to the number (or count) of the plurality ofcardiac motion phases. In some embodiments, in each cardiac cycle, thenumber (or count) of the quality indexes with non-zero values may bedetermined. In some embodiments, an average number (or count) may bedetermined based on the total number (or count) of the quality indexeswith non-zero values in a plurality of cardiac cycles divided by thenumber (or count) of the plurality of cardiac cycles. In someembodiments, for a specific cardiac cycle, if the number (or count) ofthe quality indexes with non-zero values is no less than the averagenumber (or count), then the phase of interest of the specific cardiaccycle may be determined as:P _(c)=arg_(p)(max(average(QuaIdx_(c) ^(p)))),  (5)where c refers to the sequence number of the specific cardiac cycle inthe plurality of cardiac cycles, P_(c) refers to the phase of interestin the cth cardiac cycle, p refers to the cardiac motion phases in acardiac cycle (e.g., the cth cardiac cycle), QuaIdx refers to a qualityindex matrix, and average(QuaIdx_(c) ^(p)) refers to the average valueof the quality indexes of the images corresponding to the K layers ofthe phase p in the cth cardiac cycle.

According to the process for determining the quality index as disclosedherein, the quality index of the image may be determined moreaccurately, thereby improving the accuracy of the determination of thephase of interest of cardiac motion, and facilitating the determinationof the target image(s) of the phase of interest.

The operations illustrated above does not rely on user interfaceinteraction, and can automatically detect and extract the images to beevaluated, and automatically analyze the image quality of the images(e.g., vascular images). In coronary angiography, the process can beperformed to automatically select the phase of interest. Users (e.g.,doctors) are not required to evaluate image(s) and choose one or morephases of interest for image reconstruction. Therefore, the coronaryreconstruction process may be simplified, and the users' time toevaluate image(s) and/or select parameters (e.g., the phase of interest)may be saved.

It should be understood that although the various operations in theprocesses of FIGS. 4-12 are displayed successfully as indicated by thearrows; these operations are not necessarily performed in the orderindicated by the arrows. Except as explicitly stated herein, there is nostrict ordering of the execution of these operations, and theseoperations can be performed in other orders. Moreover, at least aportion of the operations in FIGS. 4-12 may include a plurality ofsub-steps or a plurality of stages. These sub-steps or stages are notnecessarily executed at the same time, but may be executed at differenttimes. The execution order of these sub-steps or stages is also notnecessarily successful, but may be performed alternately with otheroperations or at least a portion of the sub-steps or stages of the otheroperations.

FIG. 13A is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure. The processingdevice 140 a may include an obtaining module 13100, an image of regionof interest extraction module (or ROI image extracting module) 13200,and an image quality evaluation module 13300. In some embodiments, theprocessing device 140 a may also be referred to as an image qualityevaluation device.

The obtaining module 13100 may be configured to obtain one or moreimages to be evaluated. The ROI image extracting module 13200 may beconfigured to obtain one or more vascular images of interest accordingto the image(s) to be evaluated and/or one or more segmentationthresholds. The image quality evaluation module 13300 may be configuredto perform image quality evaluation according to the vascular images ofinterest.

More descriptions of the obtaining module 13100 may be found elsewherein the present disclosure (e.g., FIG. 4 and descriptions thereof). Moredescriptions of the ROI image extracting 13200 may be found elsewhere inthe present disclosure (e.g., FIG. 4 and descriptions thereof). Moredescriptions of the image quality evaluation module 13300 may be foundelsewhere in the present disclosure (e.g., FIG. 4 and descriptionsthereof).

For the specific descriptions of the image quality evaluation device,reference may be made to the descriptions of the image qualityevaluation process(es) illustrated above, and details will not berepeated herein. Each module in the image quality evaluation device maybe implemented in whole or in part of software, hardware, andcombinations thereof. The modules can be embedded in the hardware in theprocessor in the computing device, or may be stored in the memory of thecomputing device in the form of software, so that the processor canexecute the operations corresponding to the above modules.

FIG. 13B is a block diagram illustrating another exemplary processingdevice according to some embodiments of the present disclosure. Theprocessing device 140 b may include an image selection module 13400, aquality index determination module 13500, and an image reconstructionmodule 13600. In some embodiments, the processing device 140 b may alsobe referred to as an image reconstruction device.

The image selection module 13400 may be configured to obtain projectiondata of a plurality of cardiac motion phases, reconstruct correspondingimages of the cardiac motion phases, and/or use the reconstructed imagesas images to be evaluated. The quality index determination module 13500may be configured to determine a quality index for each image to beevaluated, based on one or more image quality evaluation rules. Theimage reconstruction module 13600 may be configured to determine a phaseof interest (in a cardiac cycle) based on the quality indexes of theimages to be evaluated, and determine one or more target images of thephase of interest.

In some embodiments, the image selection module 13400 may include: amean phase determination unit, an image of region of interest extractionunit, a blood vessel centerline extraction unit, and an image extractionunit. The mean phase determination unit may be configured to determine amean phase based on the images of the plurality of cardiac motionphases. The image of region of interest extraction unit may beconfigured to select one or more candidate cardiac motion phases in apreset phase range including the mean phase; and/or obtain an image of aregion of interest by extracting the region of interest in each initialimage of the preset range. The blood vessel centerline extraction unitmay be configured to identify a blood vessel centerline associated withthe image(s) of the region of interest. The image extraction unit may beconfigured to obtain the images to be evaluated by segmenting, based ona preset region including the blood vessel centerline, the image(s) ofthe region of interest.

More descriptions of the image selection module 13400 may be foundelsewhere in the present disclosure (e.g., FIGS. 5-11 and descriptionsthereof). More descriptions of the quality index determination module13500 may be found elsewhere in the present disclosure (e.g., FIGS. 5-6and 12, and descriptions thereof). More descriptions of the imagereconstruction module 13600 may be found elsewhere in the presentdisclosure (e.g., FIGS. 5-6 and descriptions thereof).

For the specific descriptions of the image reconstruction device,reference may be made to the descriptions of the image reconstructionprocess(es) illustrated above, and details will not be repeated herein.Each module in the image reconstruction device may be implemented inwhole or in part of software, hardware, and combinations thereof. Themodules can be embedded in the hardware in the processor in thecomputing device, or may be stored in the memory of the computing devicein the form of software, so that the processor can execute theoperations corresponding to the above modules.

It should be noted that the above description of the processing deviceis merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. For example, the processing device 140 a and the processingdevice 140 b may be integrated into a single device.

FIG. 14 is a block diagram illustrating an exemplary computing deviceaccording to some embodiments of the present disclosure.

As shown in FIG. 14, a computing device 1400 is provided. The computingdevice 1400 may be a terminal. The internal components of the computingdevice 1400 may be shown in FIG. 14. The computing device 1400 mayinclude a processor 1410, a memory, a network interface 1450, a displayscreen 1460, and an input device 1470 connected by a system bus 1420.The processor 1410 of the computing device 1400 may provide computingand/or control capabilities. The memory of the computing device 1400 mayinclude a non-volatile storage medium 1430, an internal memory 1440. Thenon-volatile storage medium 1430 may store an operating system 1431 andcomputer program(s) 1432. The internal memory 1440 may provide anenvironment for operation of the operating system 1431 and the computerprogram(s) 1432 in the non-volatile storage medium 1430. The networkinterface 1450 of the computing device 1400 may communicate with anexternal terminal via a network connection. The computer program(s) 1432may be executed by the processor 1410 to implement an imagereconstruction process. The display screen 1460 of the computing device1400 may include a liquid crystal display or an electronic ink displayscreen, and the input device 1470 of the computing device 1400 mayinclude a touch layer covered on the display screen, or may include abutton, a trajectory ball or a touchpad provided on the casing of thecomputing device. It may also be an external keyboard, trackpad, ormouse, or the like.

It will be understood by those skilled in the art that the structureshown in FIG. 14 is only a block diagram of a part of the structurerelated to the present disclosure, and does not constitute a limitationon the computing device on which the present disclosure scheme isapplied. The computing device may include more or fewer components thanthose shown in the figures, or some components may be combined, or havedifferent component arrangements.

In some embodiments, a computer apparatus is provided comprising amemory and a processor having computer program(s) stored therein. Theprocessor may implement one or more of the operations illustrated above(e.g., in FIGS. 4-12) when executing the computer program(s).

In some embodiments, a non-transitory computer readable medium storinginstructions is provided. The instructions, when executed by theprocessing device, may cause the processing device to implement one ormore operations illustrated above (e.g., in FIGS. 4-12).

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2103, Perl, COBOL2102, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, for example, aninstallation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the descriptions, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A method implemented on at least one machine eachof which has at least one processor and at least one storage device forreconstructing a target cardiac image, the target cardiac imageincluding a plurality of elements, each element of the plurality ofelements being a pixel or voxel, the method comprising: obtainingprojection data generated by an imaging device, the projection dataincluding a plurality of sub-sets of projection data, each sub-set ofprojection data corresponding to a cardiac motion phase; obtaining aplurality of cardiac images corresponding to one or more cardiac motionphases based on the plurality of sub-sets of projection datacorresponding to the one or more cardiac motion phases; determining aquality index for each cardiac image of the plurality of cardiac images;determining a phase of interest base on the plurality of qualityindexes; and obtaining the target cardiac image of the phase ofinterest.
 2. The method of claim 1, wherein the obtaining a plurality ofcardiac images corresponding to one or more cardiac motion phasescomprises: reconstructing a plurality of initial cardiac images based onthe projection data, the plurality of initial cardiac imagescorresponding to a plurality of cardiac motion phases; determining amean phase based on the plurality of initial cardiac images; selectingone or more candidate cardiac motion phases in a preset phase range, thepreset phase range including the mean phase; obtaining an image of aregion of interest by extracting the region of interest in each initialcardiac image of one or more initial cardiac images of the one or morecandidate cardiac motion phases; identifying a blood vessel centerlineassociated with the one or more images of the region of interest; andobtaining the each cardiac image of the plurality of cardiac images bysegmenting, based on a preset region including the blood vesselcenterline, each image of the region of interest.
 3. The method of claim2, wherein the determining a mean phase based on the plurality ofinitial cardiac images comprises: determining a cardiac motion parametercorresponding to each cardiac motion phase of the plurality of cardiacmotion phases based on each initial cardiac image of the plurality ofinitial cardiac images, the cardiac motion parameter being associatedwith a cardiac motion rate or intensity; and determining the mean phasebased on the plurality of cardiac motion parameters corresponding to theplurality of cardiac motion phases.
 4. The method of claim 2, whereinthe determining a mean phase based on the plurality of initial cardiacimages comprises: obtaining a plurality of mean absolute differences(MADs) by determining an MAD between two initial cardiac images of eachtwo adjacent cardiac motion phases of the plurality of cardiac motionphases; determining a plurality of cardiac motion parameterscorresponding to the plurality of cardiac motion phases based on theplurality of mean absolute differences; and determining the mean phasebased on the plurality of cardiac motion parameters corresponding to theplurality of cardiac motion phases.
 5. The method of claim 4, whereinthe determining a plurality of cardiac motion parameters correspondingto the plurality of cardiac motion phases comprises: determining a firstmean absolute difference (MAD) between a first initial cardiac image ofa first cardiac motion phase that occurs before the each cardiac motionphase and an initial cardiac image of the each cardiac motion phase;determining a second mean absolute difference (MAD) between a secondinitial cardiac image of a second cardiac motion phase that occurs afterthe each cardiac motion phase and an initial cardiac images of the eachcardiac motion phase; and designating a sum of the first mean absolutedifference (MAD) and the second mean absolute difference (MAD) as thecardiac motion parameter corresponding to the each cardiac motion phase.6. The method of claim 5, wherein the first cardiac motion phase isadjacent to the each cardiac motion phase, and the second cardiac motionphase is adjacent to the each cardiac motion phase.
 7. The method ofclaim 4, wherein the determining a mean phase comprises: designating acardiac motion phase corresponding to a minimum cardiac motion parameterin a systolic period as the mean phase in the systolic period.
 8. Themethod of claim 4, wherein the determining a mean phase comprises:designating a cardiac motion phase corresponding to a minimum cardiacmotion parameter in a diastolic period as the mean phase in thediastolic period.
 9. The method of claim 2, wherein the obtaining animage of a region of interest by extracting the region of interest ineach initial cardiac image of one or more initial cardiac images of theone or more candidate cardiac motion phases comprises: extracting aventricular image from the each initial cardiac image of the one or moreinitial cardiac images; obtaining, based on the ventricular image, afirst threshold associated with gray levels of a contrast agent in theone or more initial cardiac images; obtaining a contrast agent image bysegmenting the ventricular image based on the first threshold; andextracting the image of the region of interest from the contrast agentimage.
 10. The method of claim 9, wherein the obtaining an image of aregion of interest by extracting the region of interest in each initialcardiac image of one or more initial cardiac images of the one or morecandidate cardiac motion phases further comprises: smoothing the eachinitial cardiac image of the one or more initial cardiac images using alow pass filter.
 11. The method of claim 2, wherein the identifying ablood vessel centerline associated with the one or more images of theregion of interest comprises: generating one or more images in a coronalplane and one or more images in a sagittal plane based on the one ormore images of the region of interest; determining a blood vessel mainbody based on the one or more images in the coronal plane and the one ormore images in the sagittal plane; filtering out one or more falsepositive vessels from the blood vessel main body; and identifying, basedon the filtered blood vessel main body, a blood vessel center in eachtransverse layer associated with the one or more image of the region ofinterest.
 12. The method of claim 2, wherein the obtaining the eachcardiac image of the plurality of cardiac images by segmenting, based ona preset region including the blood vessel centerline, each image of theregion of interest comprises: generating a transformed image of theregion of interest by performing a top-hat transformation on the eachimage of the region of interest; generating an image including anintraventricular region by segmenting, based on a second thresholdassociated with gray levels of a soft tissue, the transformed image ofthe region of interest; and obtaining the each cardiac image of theplurality of cardiac images by segmenting, based on the preset regionincluding the blood vessel centerline, the image including theintraventricular region.
 13. The method of claim 1, wherein thedetermining a phase of interest base on the plurality of quality indexescomprises: determining a maximum quality index in the plurality ofquality indexes; and designating a phase of an image that has themaximum quality index as the phase of interest.
 14. The method of claim1, wherein the obtaining the target cardiac image of the phase ofinterest comprises: selecting the target cardiac image of the phase ofinterest from the plurality of cardiac images; or reconstructing thetarget cardiac image of the phase of interest based on a sub-set ofprojection data corresponding to the phase of interest.
 15. The methodof claim 1, wherein the determining a quality index for each cardiacimage of the plurality of cardiac images comprises: determining, basedon a maximum gray level of the plurality of elements of the each cardiacimage, one or more thresholds for segmenting the each cardiac image;determining one or more sub-images of a region of interest bysegmenting, based on the one or more thresholds, the each cardiac image;and determining, based on the one or more sub-images of the region ofinterest, a quality index for the each cardiac image.
 16. A system forreconstructing a target cardiac image, comprising: at least one storagedevice storing a set of instructions; and at least one processor incommunication with the storage device, wherein when executing the set ofinstructions, the at least one processor is configured to cause thesystem to perform operations including: obtaining projection datagenerated by an imaging device, the projection data including aplurality of sub-sets of projection data, each sub-set of projectiondata corresponding to a cardiac motion phase; obtaining a plurality ofcardiac images corresponding to one or more cardiac motion phases basedon the plurality of sub-sets of projection data corresponding to the oneor more cardiac motion phases; determining a quality index for eachcardiac image of the plurality of cardiac images; determining a phase ofinterest base on the plurality of quality indexes; and obtaining thetarget cardiac image of the phase of interest.
 17. A non-transitorycomputer readable medium storing instructions, the instructions, whenexecuted by at least one processor, causing the at least one processorto implement a method comprising: obtaining projection data generated byan imaging device, the projection data including a plurality of sub-setsof projection data, each sub-set of projection data corresponding to acardiac motion phase; obtaining a plurality of cardiac imagescorresponding to one or more cardiac motion phases based on theplurality of sub-sets of projection data corresponding to the one ormore cardiac motion phases; determining a quality index for each cardiacimage of the plurality of cardiac images; determining a phase ofinterest base on the plurality of quality indexes; and obtaining thetarget cardiac image of the phase of interest.
 18. The system of claim16, wherein to obtain a plurality of cardiac images corresponding to oneor more cardiac motion phases, the at least one processor is configuredto cause the system to perform operations including: reconstructing aplurality of initial cardiac images based on the projection data, theplurality of initial cardiac images corresponding to a plurality ofcardiac motion phases; determining a mean phase based on the pluralityof initial cardiac images; selecting one or more candidate cardiacmotion phases in a preset phase range, the preset phase range includingthe mean phase; obtaining an image of a region of interest by extractingthe region of interest in each initial cardiac image of one or moreinitial cardiac images of the one or more candidate cardiac motionphases; identifying a blood vessel centerline associated with the one ormore images of the region of interest; and obtaining the each cardiacimage of the plurality of cardiac images by segmenting, based on apreset region including the blood vessel centerline, each image of theregion of interest.
 19. The system of claim 18, wherein to determine amean phase based on the plurality of initial cardiac images, the atleast one processor is configured to cause the system to performoperations including: determining a cardiac motion parametercorresponding to each cardiac motion phase of the plurality of cardiacmotion phases based on each initial cardiac image of the plurality ofinitial cardiac images, the cardiac motion parameter being associatedwith a cardiac motion rate or intensity; and determining the mean phasebased on the plurality of cardiac motion parameters corresponding to theplurality of cardiac motion phases.
 20. The system of claim 18, whereinto determine a mean phase based on the plurality of initial cardiacimages, the at least one processor is configured to cause the system toperform operations including: obtaining a plurality of mean absolutedifferences (MADs) by determining an MAD between two initial cardiacimages of each two adjacent cardiac motion phases of the plurality ofcardiac motion phases; determining a plurality of cardiac motionparameters corresponding to the plurality of cardiac motion phases basedon the plurality of mean absolute differences; and determining the meanphase based on the plurality of cardiac motion parameters correspondingto the plurality of cardiac motion phases.